Active marks structure optimization for optical-electronic systems of spatial position control of industrial objects

NASA Astrophysics Data System (ADS)

Sycheva, Elena A.; Vasilev, Aleksandr S.; Lashmanov, Oleg U.; Korotaev, Valery V.

2017-06-01

The article is devoted to the optimization of optoelectronic systems of the spatial position of objects. Probabilistic characteristics of the detection of an active structured mark on a random noisy background are investigated. The developed computer model and the results of the study allow us to estimate the probabilistic characteristics of detection of a complex structured mark on a random gradient background, and estimate the error of spatial coordinates. The results of the study make it possible to improve the accuracy of measuring the coordinates of the object. Based on the research recommendations are given on the choice of parameters of the optimal mark structure for use in opticalelectronic systems for monitoring the spatial position of large-sized structures.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

PubMed

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

Optofluidic microscope with 3D spatial resolution.

PubMed

Vig, Asger Laurburg; Marie, Rodolphe; Jensen, Eric; Kristensen, Anders

2010-03-01

This paper reports on-chip based optical detection with three-dimensional spatial resolution by integration of an optofluidic microscope (OFM) in a microfluidic pinched flow fractionation (PFF) separation device. This setup also enables on-chip particle image velocimetry (PIV). The position in the plane perpendicular to the flow direction and the velocity along the flow direction of separated fluorescent labeled polystyrene microspheres with diameters of 1 microm , 2.1 microm , 3 microm and 4 microm is determined by the OFM. These results are bench marked against those obtained with a PFF device using conventional fluorescence microscope readout. The size separated microspheres are detected by OFM with an accuracy of

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

PubMed

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Privacy protection versus cluster detection in spatial epidemiology.

PubMed

Olson, Karen L; Grannis, Shaun J; Mandl, Kenneth D

2006-11-01

Patient data that includes precise locations can reveal patients' identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Automated reference-free detection of motion artifacts in magnetic resonance images.

PubMed

Küstner, Thomas; Liebgott, Annika; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz; Gatidis, Sergios

2018-04-01

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis. On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively. Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

A scale-invariant change detection method for land use/cover change research

NASA Astrophysics Data System (ADS)

Xing, Jin; Sieber, Renee; Caelli, Terrence

2018-07-01

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

Performance map of a cluster detection test using extended power

PubMed Central

2013-01-01

Background Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. Methods To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. Results Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. Conclusions The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. PMID:24156765

An integrated theory of attention and decision making in visual signal detection.

PubMed

Smith, Philip L; Ratcliff, Roger

2009-04-01

The simplest attentional task, detecting a cued stimulus in an otherwise empty visual field, produces complex patterns of performance. Attentional cues interact with backward masks and with spatial uncertainty, and there is a dissociation in the effects of these variables on accuracy and on response time. A computational theory of performance in this task is described. The theory links visual encoding, masking, spatial attention, visual short-term memory (VSTM), and perceptual decision making in an integrated dynamic framework. The theory assumes that decisions are made by a diffusion process driven by a neurally plausible, shunting VSTM. The VSTM trace encodes the transient outputs of early visual filters in a durable form that is preserved for the time needed to make a decision. Attention increases the efficiency of VSTM encoding, either by increasing the rate of trace formation or by reducing the delay before trace formation begins. The theory provides a detailed, quantitative account of attentional effects in spatial cuing tasks at the level of response accuracy and the response time distributions. (c) 2009 APA, all rights reserved

Attentional Mechanisms in Simple Visual Detection: A Speed-Accuracy Trade-Off Analysis

ERIC Educational Resources Information Center

Liu, Charles C.; Wolfgang, Bradley J.; Smith, Philip L.

2009-01-01

Recent spatial cuing studies have shown that detection sensitivity can be increased by the allocation of attention. This increase has been attributed to one of two mechanisms: signal enhancement or uncertainty reduction. Signal enhancement is an increase in the signal-to-noise ratio at the cued location; uncertainty reduction is a reduction in the…

An Integrated Theory of Attention and Decision Making in Visual Signal Detection

ERIC Educational Resources Information Center

Smith, Philip L.; Ratcliff, Roger

2009-01-01

The simplest attentional task, detecting a cued stimulus in an otherwise empty visual field, produces complex patterns of performance. Attentional cues interact with backward masks and with spatial uncertainty, and there is a dissociation in the effects of these variables on accuracy and on response time. A computational theory of performance in…

Psychophysical and perceptual performance in a simulated-scotoma model of human eye injury

NASA Astrophysics Data System (ADS)

Brandeis, R.; Egoz, I.; Peri, D.; Sapiens, N.; Turetz, J.

2008-02-01

Macular scotomas, affecting visual functioning, characterize many eye and neurological diseases like AMD, diabetes mellitus, multiple sclerosis, and macular hole. In this work, foveal visual field defects were modeled, and their effects were evaluated on spatial contrast sensitivity and a task of stimulus detection and aiming. The modeled occluding scotomas, of different size, were superimposed on the stimuli presented on the computer display, and were stabilized on the retina using a mono Purkinje Eye-Tracker. Spatial contrast sensitivity was evaluated using square-wave grating stimuli, whose contrast thresholds were measured using the method of constant stimuli with "catch trials". The detection task consisted of a triple conjunctive visual search display of: size (in visual angle), contrast and background (simple, low-level features vs. complex, high-level features). Search/aiming accuracy as well as R.T. measures used for performance evaluation. Artificially generated scotomas suppressed spatial contrast sensitivity in a size dependent manner, similar to previous studies. Deprivation effect was dependent on spatial frequency, consistent with retinal inhomogeneity models. Stimulus detection time was slowed in complex background search situation more than in simple background. Detection speed was dependent on scotoma size and size of stimulus. In contrast, visually guided aiming was more sensitive to scotoma effect in simple background search situation than in complex background. Both stimulus aiming R.T. and accuracy (precision targeting) were impaired, as a function of scotoma size and size of stimulus. The data can be explained by models distinguishing between saliency-based, parallel and serial search processes, guiding visual attention, which are supported by underlying retinal as well as neural mechanisms.

Detection of movement intention from single-trial movement-related cortical potentials

NASA Astrophysics Data System (ADS)

Niazi, Imran Khan; Jiang, Ning; Tiberghien, Olivier; Feldbæk Nielsen, Jørgen; Dremstrup, Kim; Farina, Dario

2011-10-01

Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of -66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.

Using a high spatial resolution tactile sensor for intention detection.

PubMed

Castellini, Claudio; Koiva, Risto

2013-06-01

Intention detection is the interpretation of biological signals with the aim of automatically, reliably and naturally understanding what a human subject desires to do. Although intention detection is not restricted to disabled people, such methods can be crucial in improving a patient's life, e.g., aiding control of a robotic wheelchair or of a self-powered prosthesis. Traditionally, intention detection is done using, e.g., gaze tracking, surface electromyography and electroencephalography. In this paper we present exciting initial results of an experiment aimed at intention detection using a high-spatial-resolution, high-dynamic-range tactile sensor. The tactile image of the ventral side of the forearm of 9 able-bodied participants was recorded during a variable-force task stimulated at the fingertip. Both the forces at the fingertip and at the forearm were synchronously recorded. We show that a standard dimensionality reduction technique (Principal Component Analysis) plus a Support Vector Machine attain almost perfect detection accuracy of the direction and the intensity of the intended force. This paves the way for high spatial resolution tactile sensors to be used as a means for intention detection.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Change deafness for real spatialized environmental scenes.

PubMed

Gaston, Jeremy; Dickerson, Kelly; Hipp, Daniel; Gerhardstein, Peter

2017-01-01

The everyday auditory environment is complex and dynamic; often, multiple sounds co-occur and compete for a listener's cognitive resources. 'Change deafness', framed as the auditory analog to the well-documented phenomenon of 'change blindness', describes the finding that changes presented within complex environments are often missed. The present study examines a number of stimulus factors that may influence change deafness under real-world listening conditions. Specifically, an AX (same-different) discrimination task was used to examine the effects of both spatial separation over a loudspeaker array and the type of change (sound source additions and removals) on discrimination of changes embedded in complex backgrounds. Results using signal detection theory and accuracy analyses indicated that, under most conditions, errors were significantly reduced for spatially distributed relative to non-spatial scenes. A second goal of the present study was to evaluate a possible link between memory for scene contents and change discrimination. Memory was evaluated by presenting a cued recall test following each trial of the discrimination task. Results using signal detection theory and accuracy analyses indicated that recall ability was similar in terms of accuracy, but there were reductions in sensitivity compared to previous reports. Finally, the present study used a large and representative sample of outdoor, urban, and environmental sounds, presented in unique combinations of nearly 1000 trials per participant. This enabled the exploration of the relationship between change perception and the perceptual similarity between change targets and background scene sounds. These (post hoc) analyses suggest both a categorical and a stimulus-level relationship between scene similarity and the magnitude of change errors.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

NASA Astrophysics Data System (ADS)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Privacy Protection Versus Cluster Detection in Spatial Epidemiology

PubMed Central

Olson, Karen L.; Grannis, Shaun J.; Mandl, Kenneth D.

2006-01-01

Objectives. Patient data that includes precise locations can reveal patients’ identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. Methods. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. Results. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. Conclusions. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies. PMID:17018828

Research on Quantum Algorithms at the Institute for Quantum Information

DTIC Science & Technology

2009-10-17

accuracy threshold theorem for the one-way quantum computer. Their proof is based on a novel scheme, in which a noisy cluster state in three spatial...detected. The proof applies to independent stochastic noise but (in contrast to proofs of the quantum accuracy threshold theorem based on concatenated...proved quantum threshold theorems for long-range correlated non-Markovian noise, for leakage faults, for the one-way quantum computer, for postselected

The effects of display time and eccentricity on the detection of amplitude and phase degradations in textured stimuli.

PubMed

Clarke, Alasdair D F; Green, Patrick R; Chantler, Mike J

2012-03-08

The amplitude and phase spectra of an image contain important information for perception, and a large body of work has investigated the effects of manipulating these spectra on the recognition or classification of image content. Here, we use a novel means of investigating sensitivity to amplitude and phase spectra properties, testing the ability of observers to detect degradations of the spectral content of synthetic images of textured surfaces that are broadband in the frequency domain. The effects of display time and retinal eccentricity on sensitivity to these two manipulations are compared using stimuli matched for difficulty of detection. We find no difference between the time courses for the detection of degradation in the two spectra; in both cases, accuracy rises above chance when display times are greater than 80 ms. Increasing retinal eccentricity to 8.7°, however, has a significantly stronger effect on the accuracy of detecting degradations of the amplitude spectrum than of the phase spectrum. Further, sensitivity to phase randomization that is restricted to low spatial frequencies is greater in the periphery (at 8.7° eccentricity) than in the fovea. These last two results imply that the fovea and periphery are specialized for the processing of phase spectrum information in distinct spatial frequency bands.

Optical Detection of Lightning from Space

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.; Christian, Hugh J.

1998-01-01

Optical sensors have been developed to detect lightning from space during both day and night. These sensors have been fielded in two existing satellite missions and may be included on a third mission in 2002. Satellite-hosted, optically-based lightning detection offers three unique capabilities: (1) the ability to reliably detect lightning over large, often remote, spatial regions, (2) the ability to sample all (IC and CG) lightning, and (3) the ability to detect lightning with uniform (i.e., not range-dependent) sensitivity or detection efficiency. These represent significant departures from conventional RF-based detection techniques, which typically have strong range dependencies (biases) or range limitations in their detection capabilities. The atmospheric electricity team of the NASA Marshall Space Flight Center's Global Hydrology and Climate Center has implemented a three-step satellite lightning research program which includes three phases: proof-of-concept/climatology, science algorithm development, and operational application. The first instrument in the program, the Optical Transient Detector (OTD), is deployed on a low-earth orbit (LEO) satellite with near-polar inclination, yielding global coverage. The sensor has a 1300 x 1300 sq km field of view (FOV), moderate detection efficiency, moderate localization accuracy, and little data bias. The OTD is a proof-of-concept instrument and its mission is primarily a global lightning climatology. The limited spatial accuracy of this instrument makes it suboptimal for use in case studies, although significant science knowledge has been gained from the instrument as deployed.

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of false- negative and false-positive regions

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Donovan, Tim; Brennan, Patrick C.; Dix, Alan; Manning, David J.

2011-03-01

Aim: To optimize automated classification of radiological errors during lung nodule detection from chest radiographs (CxR) using a support vector machine (SVM) run on the spatial frequency features extracted from the local background of selected regions. Background: The majority of the unreported pulmonary nodules are visually detected but not recognized; shown by the prolonged dwell time values at false-negative regions. Similarly, overestimated nodule locations are capturing substantial amounts of foveal attention. Spatial frequency properties of selected local backgrounds are correlated with human observer responses either in terms of accuracy in indicating abnormality position or in the precision of visual sampling the medical images. Methods: Seven radiologists participated in the eye tracking experiments conducted under conditions of pulmonary nodule detection from a set of 20 postero-anterior CxR. The most dwelled locations have been identified and subjected to spatial frequency (SF) analysis. The image-based features of selected ROI were extracted with un-decimated Wavelet Packet Transform. An analysis of variance was run to select SF features and a SVM schema was implemented to classify False-Negative and False-Positive from all ROI. Results: A relative high overall accuracy was obtained for each individually developed Wavelet-SVM algorithm, with over 90% average correct ratio for errors recognition from all prolonged dwell locations. Conclusion: The preliminary results show that combined eye-tracking and image-based features can be used for automated detection of radiological error with SVM. The work is still in progress and not all analytical procedures have been completed, which might have an effect on the specificity of the algorithm.

Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence

PubMed Central

Ohyama, Junji; Watanabe, Katsumi

2016-01-01

We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images. PMID:26869966

Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence.

PubMed

Ohyama, Junji; Watanabe, Katsumi

2016-01-01

We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images.

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cecil, Daniel J.; Cummins, Kenneth L.; Petersen, Walter A.; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes

USGS Publications Warehouse

Kokaly, Raymond F.; Couvillion, Brady; Holloway, JoAnn M.; Roberts, Dar A.; Ustin, Susan L.; Peterson, Seth H.; Khanna, Shruti; Piazza, Sarai C.

2013-01-01

We applied a spectroscopic analysis to Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected from low and medium altitudes during and after the Deepwater Horizon oil spill to delineate the distribution of oil-damaged canopies in the marshes of Barataria Bay, Louisiana. Spectral feature analysis compared the AVIRIS data to reference spectra of oiled marsh by using absorption features centered near 1.7 and 2.3 μm, which arise from CH bonds in oil. AVIRIS-derived maps of oiled shorelines from the individual dates of July 31, September 14, and October 4, 2010, were 89.3%, 89.8%, and 90.6% accurate, respectively. A composite map at 3.5 m grid spacing, accumulated from the three dates, was 93.4% accurate in detecting oiled shorelines. The composite map had 100% accuracy for detecting damaged plant canopy in oiled areas that extended more than 1.2 m into the marsh. Spatial resampling of the AVIRIS data to 30 m reduced the accuracy to 73.6% overall. However, detection accuracy remained high for oiled canopies that extended more than 4 m into the marsh (23 of 28 field reference points with oil were detected). Spectral resampling of the 3.5 m AVIRIS data to Landsat Enhanced Thematic Mapper (ETM) spectral response greatly reduced the detection of oil spectral signatures. With spatial resampling of simulated Landsat ETM data to 30 m, oil signatures were not detected. Overall, ~ 40 km of coastline, marsh comprised mainly of Spartina alterniflora and Juncus roemerianus, were found to be oiled in narrow zones at the shorelines. Zones of oiled canopies reached on average 11 m into the marsh, with a maximum reach of 21 m. The field and airborne data showed that, in many areas, weathered oil persisted in the marsh from the first field survey, July 10, to the latest airborne survey, October 4, 2010. The results demonstrate the applicability of high spatial resolution imaging spectrometer data to identifying contaminants in the environment for use in evaluating ecosystem disturbance and response.

Saliency detection using mutual consistency-guided spatial cues combination

NASA Astrophysics Data System (ADS)

Wang, Xin; Ning, Chen; Xu, Lizhong

2015-09-01

Saliency detection has received extensive interests due to its remarkable contribution to wide computer vision and pattern recognition applications. However, most existing computational models are designed for detecting saliency in visible images or videos. When applied to infrared images, they may suffer from limitations in saliency detection accuracy and robustness. In this paper, we propose a novel algorithm to detect visual saliency in infrared images by mutual consistency-guided spatial cues combination. First, based on the luminance contrast and contour characteristics of infrared images, two effective saliency maps, i.e., the luminance contrast saliency map and contour saliency map are constructed, respectively. Afterwards, an adaptive combination scheme guided by mutual consistency is exploited to integrate these two maps to generate the spatial saliency map. This idea is motivated by the observation that different maps are actually related to each other and the fusion scheme should present a logically consistent view of these maps. Finally, an enhancement technique is adopted to incorporate spatial saliency maps at various scales into a unified multi-scale framework to improve the reliability of the final saliency map. Comprehensive evaluations on real-life infrared images and comparisons with many state-of-the-art saliency models demonstrate the effectiveness and superiority of the proposed method for saliency detection in infrared images.

Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

PubMed Central

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy. PMID:22163641

Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

PubMed

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.

2015-12-01

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

Impact Induced Delamination Detection and Quantification With Guided Wavefield Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara A. C.; Yu, Lingyu; Seebo, Jeffrey P.

2015-01-01

This paper studies impact induced delamination detection and quantification by using guided wavefield data and spatial wavenumber imaging. The complex geometry impact-like delamination is created through a quasi-static indentation on a CFRP plate. To detect and quantify the impact delamination in the CFRP plate, PZT-SLDV sensing and spatial wavenumber imaging are performed. In the PZT-SLDV sensing, the guided waves are generated from the PZT, and the high spatial resolution guided wavefields are measured by the SLDV. The guided wavefield data acquired from the PZT-SLDV sensing represent guided wave propagation in the composite laminate and include guided wave interaction with the delamination damage. The measured guided wavefields are analyzed through the spatial wavenumber imaging method, which generates an image containing the dominant local wavenumber at each spatial location. The spatial wavenumber imaging result for the simple single layer Teflon insert delamination provided quantitative information on delamination damage size and location. The location of delamination damage is indicated by the area with larger wavenumbers in the spatial wavenumber image. The impact-like delamination results only partially agreed with the damage size and shape. The results also demonstrated the dependence on excitation frequency. Future work will further investigate the accuracy of the wavenumber imaging method for real composite damage and the dependence on frequency of excitation.

High-field fMRI unveils orientation columns in humans.

PubMed

Yacoub, Essa; Harel, Noam; Ugurbil, Kâmil

2008-07-29

Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the possibility of detecting small neuronal ensembles that constitute fundamental computational units in the brain, such as cortical columns. Orientation columns in visual cortex are perhaps the best known example of such a functional organization in the brain. They cannot be discerned via anatomical characteristics, as with ocular dominance columns. Instead, the elucidation of their organization requires functional imaging methods. However, because of insufficient sensitivity, spatial accuracy, and image resolution of the available mapping techniques, thus far, they have not been detected in humans. Here, we demonstrate, by using high-field (7-T) fMRI, the existence and spatial features of orientation- selective columns in humans. Striking similarities were found with the known spatial features of these columns in monkeys. In addition, we found that a larger number of orientation columns are devoted to processing orientations around 90 degrees (vertical stimuli with horizontal motion), whereas relatively similar fMRI signal changes were observed across any given active column. With the current proliferation of high-field MRI systems and constant evolution of fMRI techniques, this study heralds the exciting prospect of exploring unmapped and/or unknown columnar level functional organizations in the human brain.

Space-based Swath Imaging Laser Altimeter for Cryospheric Topographic and Surface Property Mapping

NASA Technical Reports Server (NTRS)

Abshire, James; Harding, David; Shuman, Chris; Sun, Xiaoli; Dabney, Phil; Krainak, Michael; Scambos, Ted

2005-01-01

Uncertainties in the response of the Greenland and Antarctic polar ice sheets to global climatic change inspired the development of ICESat/GLAS as part of NASA's Earth Observing System. ICESat's primary purpose is the measurement of ice sheet surface elevation profiles with sufficient accuracy, spatial density, and temporal coverage so that elevation changes can be derived with an accuracy of <1.5 cm/year for averages of measurements over the ice sheets with areas of 100 x 100 km. The primary means to achieve this elevation change detection is spatial averaging of elevation differences at cross-overs between ascending and descending profiles in areas of low ice surface slope. Additional information is included in the original extended abstract.

MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

PubMed

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.

MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches

PubMed Central

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm2 to 30 cm2, whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered. PMID:23418485

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Xue, Hui; Jolly, Marie-Pierre; Guetter, Christoph; Kellman, Peter; Hsu, Li-Yueh; Arai, Andrew; Zuehlsdorff, Sven; Littmann, Arne; Georgescu, Bogdan; Guehring, Jens

2011-03-01

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Single photon detection and timing in the Lunar Laser Ranging Experiment.

NASA Technical Reports Server (NTRS)

Poultney, S. K.

1972-01-01

The goals of the Lunar Laser Ranging Experiment lead to the need for the measurement of a 2.5 sec time interval to an accuracy of a nanosecond or better. The systems analysis which included practical retroreflector arrays, available laser systems, and large telescopes led to the necessity of single photon detection. Operation under all background illumination conditions required auxiliary range gates and extremely narrow spectral and spatial filters in addition to the effective gate provided by the time resolution. Nanosecond timing precision at relatively high detection efficiency was obtained using the RCA C31000F photomultiplier and Ortec 270 constant fraction of pulse-height timing discriminator. The timing accuracy over the 2.5 sec interval was obtained using a digital interval with analog vernier ends. Both precision and accuracy are currently checked internally using a triggerable, nanosecond light pulser. Future measurements using sub-nanosecond laser pulses will be limited by the time resolution of single photon detectors.

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

NASA Astrophysics Data System (ADS)

Matongera, Trylee Nyasha; Mutanga, Onisimo; Dube, Timothy; Sibanda, Mbulisi

2017-05-01

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = -10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.

Development and validation of an automated and marker-free CT-based spatial analysis method (CTSA) for assessment of femoral hip implant migration: In vitro accuracy and precision comparable to that of radiostereometric analysis (RSA).

PubMed

Scheerlinck, Thierry; Polfliet, Mathias; Deklerck, Rudi; Van Gompel, Gert; Buls, Nico; Vandemeulebroucke, Jef

2016-01-01

We developed a marker-free automated CT-based spatial analysis (CTSA) method to detect stem-bone migration in consecutive CT datasets and assessed the accuracy and precision in vitro. Our aim was to demonstrate that in vitro accuracy and precision of CTSA is comparable to that of radiostereometric analysis (RSA). Stem and bone were segmented in 2 CT datasets and both were registered pairwise. The resulting rigid transformations were compared and transferred to an anatomically sound coordinate system, taking the stem as reference. This resulted in 3 translation parameters and 3 rotation parameters describing the relative amount of stem-bone displacement, and it allowed calculation of the point of maximal stem migration. Accuracy was evaluated in 39 comparisons by imposing known stem migration on a stem-bone model. Precision was estimated in 20 comparisons based on a zero-migration model, and in 5 patients without stem loosening. Limits of the 95% tolerance intervals (TIs) for accuracy did not exceed 0.28 mm for translations and 0.20° for rotations (largest standard deviation of the signed error (SD(SE)): 0.081 mm and 0.057°). In vitro, limits of the 95% TI for precision in a clinically relevant setting (8 comparisons) were below 0.09 mm and 0.14° (largest SD(SE): 0.012 mm and 0.020°). In patients, the precision was lower, but acceptable, and dependent on CT scan resolution. CTSA allows detection of stem-bone migration with an accuracy and precision comparable to that of RSA. It could be valuable for evaluation of subtle stem loosening in clinical practice.

Common mechanisms of spatial attention in memory and perception: a tactile dual-task study.

PubMed

Katus, Tobias; Andersen, Søren K; Müller, Matthias M

2014-03-01

Orienting attention to locations in mnemonic representations engages processes that functionally and anatomically overlap the neural circuitry guiding prospective shifts of spatial attention. The attention-based rehearsal account predicts that the requirement to withdraw attention from a memorized location impairs memory accuracy. In a dual-task study, we simultaneously presented retro-cues and pre-cues to guide spatial attention in short-term memory (STM) and perception, respectively. The spatial direction of each cue was independent of the other. The locations indicated by the combined cues could be compatible (same hand) or incompatible (opposite hands). Incompatible directional cues decreased lateralized activity in brain potentials evoked by visual cues, indicating interference in the generation of prospective attention shifts. The detection of external stimuli at the prospectively cued location was impaired when the memorized location was part of the perceptually ignored hand. The disruption of attention-based rehearsal by means of incompatible pre-cues reduced memory accuracy and affected encoding of tactile test stimuli at the retrospectively cued hand. These findings highlight the functional significance of spatial attention for spatial STM. The bidirectional interactions between both tasks demonstrate that spatial attention is a shared neural resource of a capacity-limited system that regulates information processing in internal and external stimulus representations.

Topography-based analysis of Hurricane Katrina inundation of New Orleans: Chapter 3G in Science and the storms-the USGS response to the hurricanes of 2005

USGS Publications Warehouse

Gesch, Dean

2007-01-01

The ready availability of high-resolution, high-accuracy elevation data proved valuable for development of topographybased products to determine rough estimates of the inundation of New Orleans, La., from Hurricane Katrina. Because of its high level of spatial detail and vertical accuracy of elevation measurements, light detection and ranging (lidar) remote sensing is an excellent mapping technology for use in low-relief hurricane-prone coastal areas.

Combined optical coherence tomography and optical coherence elastography for glomerulonephritis classification

NASA Astrophysics Data System (ADS)

Liu, Chih-Hao; Du, Yong; Singh, Manmohan; Wu, Chen; Han, Zhaolong; Li, Jiasong; Mohammadzai, Qais; Raghunathan, Raksha; Hsu, Thomas; Noorani, Shezaan; Chang, Anthony; Mohan, Chandra; Larin, Kirill V.

2016-03-01

Acute Glomerulonephritis caused by anti-glomerular basement membrane disease has a high mortality due to delayed diagnosis. Thus, an accurate and early diagnosis is critical for preserving renal function. Currently, blood, urine, and tissue-based diagnoses can be time consuming, while ultrasound and CT imaging have relatively low spatial resolution. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides superior spatial resolution (micron scale) as compared to ultrasound and CT. Pathological changes in tissue properties can be detected based on the optical metrics analyzed from the OCT signal, such as optical attenuation and speckle variance. Moreover, OCT does not rely on ionizing radiation as with CT imaging. In addition to structural changes, the elasticity of the kidney can significantly change due to nephritis. In this work, we utilized OCT to detect the difference in tissue properties between healthy and nephritic murine kidneys. Although OCT imaging could identify the diseased tissue, classification accuracy using only optical metrics was clinically inadequate. By combining optical metrics with elasticity, the classification accuracy improved from 76% to 95%. These results show that OCT combined with OCE can be potentially useful for nephritis detection.

SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

Zhao, J.; Huang, G.; Zhao, Z.

2018-04-01

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Visual-Spatial Attention Aids the Maintenance of Object Representations in Visual Working Memory

PubMed Central

Williams, Melonie; Pouget, Pierre; Boucher, Leanne; Woodman, Geoffrey F.

2013-01-01

Theories have proposed that the maintenance of object representations in visual working memory is aided by a spatial rehearsal mechanism. In this study, we used two different approaches to test the hypothesis that overt and covert visual-spatial attention mechanisms contribute to the maintenance of object representations in visual working memory. First, we tracked observers’ eye movements while remembering a variable number of objects during change-detection tasks. We observed that during the blank retention interval, participants spontaneously shifted gaze to the locations that the objects had occupied in the memory array. Next, we hypothesized that if attention mechanisms contribute to the maintenance of object representations, then drawing attention away from the object locations during the retention interval would impair object memory during these change-detection tasks. Supporting this prediction, we found that attending to the fixation point in anticipation of a brief probe stimulus during the retention interval reduced change-detection accuracy even on the trials in which no probe occurred. These findings support models of working memory in which visual-spatial selection mechanisms contribute to the maintenance of object representations. PMID:23371773

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

MAPPING SPATIAL THEMATIC ACCURACY WITH FUZZY SETS

EPA Science Inventory

Thematic map accuracy is not spatially homogenous but variable across a landscape. Properly analyzing and representing spatial pattern and degree of thematic map accuracy would provide valuable information for using thematic maps. However, current thematic map accuracy measures (...

Evaluation of coded aperture radiation detectors using a Bayesian approach

NASA Astrophysics Data System (ADS)

Miller, Kyle; Huggins, Peter; Labov, Simon; Nelson, Karl; Dubrawski, Artur

2016-12-01

We investigate tradeoffs arising from the use of coded aperture gamma-ray spectrometry to detect and localize sources of harmful radiation in the presence of noisy background. Using an example application scenario of area monitoring and search, we empirically evaluate weakly supervised spectral, spatial, and hybrid spatio-spectral algorithms for scoring individual observations, and two alternative methods of fusing evidence obtained from multiple observations. Results of our experiments confirm the intuition that directional information provided by spectrometers masked with coded aperture enables gains in source localization accuracy, but at the expense of reduced probability of detection. Losses in detection performance can however be to a substantial extent reclaimed by using our new spatial and spatio-spectral scoring methods which rely on realistic assumptions regarding masking and its impact on measured photon distributions.

The spectral changes of deforestation in the Brazilian tropical savanna.

PubMed

Trancoso, Ralph; Sano, Edson E; Meneses, Paulo R

2015-01-01

The Cerrado is a biome in Brazil that is experiencing the most rapid loss in natural vegetation. The objective of this study was to analyze the changes in the spectral response in the red, near infrared (NIR), middle infrared (MIR), and normalized difference vegetation index (NDVI) when native vegetation in the Cerrado is deforested. The test sites were regions of the Cerrado located in the states of Bahia, Minas Gerais, and Mato Grosso. For each region, a pair of Landsat Thematic Mapper (TM) scenes from 2008 (before deforestation) and 2009 (after deforestation) was compared. A set of 1,380 samples of deforested polygons and an equal number of samples of native vegetation have their spectral properties statistically analyzed. The accuracy of deforestation detections was also evaluated using high spatial resolution imagery. Results showed that the spectral data of deforested areas and their corresponding native vegetation were statistically different. The red band showed the highest difference between the reflectance data from deforested areas and native vegetation, while the NIR band showed the lowest difference. A consistent pattern of spectral change when native vegetation in the Cerrado is deforested was identified regardless of the location in the biome. The overall accuracy of deforestation detections was 97.75%. Considering both the marked pattern of spectral changes and the high deforestation detection accuracy, this study suggests that deforestation in Cerrado can be accurately monitored, but a strong seasonal and spatial variability of spectral changes might be expected.

Spatially resolved measurement of singlet delta oxygen by radar resonance-enhanced multiphoton ionization.

PubMed

Wu, Yue; Zhang, Zhili; Ombrello, Timothy M

2013-07-01

Coherent microwave Rayleigh scattering (Radar) from resonance-enhanced multiphoton ionization (REMPI) was demonstrated to directly and nonintrusively measure singlet delta oxygen, O(2)(a(1)Δ(g)), with high spatial resolution. Two different approaches, photodissociation of ozone and microwave discharge plasma in an argon and oxygen flow, were utilized for O(2)(a(1)Δ(g)) generation. The d(1)Π(g)←a(1)Δ(g) (3-0) and d(1)Π(g)←a(1)Δ(g) (1-0) bands of O(2)(a(1)Δ(g)) were detected by Radar REMPI for two different flow conditions. Quantitative absorption measurements using sensitive off-axis integrated cavity output spectroscopy (ICOS) was used simultaneously to evaluate the accuracy and sensitivity of the Radar REMPI technique. The detection limit of Radar REMPI was found to be comparable to the ICOS technique with a detection threshold of approximately 10(14) molecules/cm(3) but with a spatial resolution that was 8 orders of magnitude smaller than the ICOS technique.

Action change detection in video using a bilateral spatial-temporal constraint

NASA Astrophysics Data System (ADS)

Tian, Jing; Chen, Li

2016-08-01

Action change detection in video aims to detect action discontinuity in video. The silhouettes-based features are desirable for action change detection. This paper studies the problem of silhouette-quality assessment. For that, a non-reference approach without the need for ground truth is proposed in this paper to evaluate the quality of silhouettes, by exploiting both the boundary contrast of the silhouettes in the spatial domain and the consistency of the silhouettes in the temporal domain. This is in contrast to that either only spatial information or only temporal information of silhouettes is exploited in conventional approaches. Experiments are conducted using artificially generated degraded silhouettes to show that the proposed approach outperforms conventional approaches to achieve more accurate quality assessment. Furthermore, experiments are performed to show that the proposed approach is able to improve the accuracy performance of conventional action change approaches in two human action video data-sets. The average runtime of the proposed approach for Weizmann action video data-set is 0.08 second for one frame using Matlab programming language. It is computationally efficient and potential to real-time implementations.

Two-stream Convolutional Neural Network for Methane Emissions Quantification

NASA Astrophysics Data System (ADS)

Wang, J.; Ravikumar, A. P.; McGuire, M.; Bell, C.; Tchapmi, L. P.; Brandt, A. R.

2017-12-01

Methane, a key component of natural gas, has a 25x higher global warming potential than carbon dioxide on a 100-year basis. Accurately monitoring and mitigating methane emissions require cost-effective detection and quantification technologies. Optical gas imaging, one of the most commonly used leak detection technology, adopted by Environmental Protection Agency, cannot estimate leak-sizes. In this work, we harness advances in computer science to allow for rapid and automatic leak quantification. Particularly, we utilize two-stream deep Convolutional Networks (ConvNets) to estimate leak-size by capturing complementary spatial information from still plume frames, and temporal information from plume motion between frames. We build large leak datasets for training and evaluating purposes by collecting about 20 videos (i.e. 397,400 frames) of leaks. The videos were recorded at six distances from the source, covering 10 -60 ft. Leak sources included natural gas well-heads, separators, and tanks. All frames were labeled with a true leak size, which has eight levels ranging from 0 to 140 MCFH. Preliminary analysis shows that two-stream ConvNets provides significant accuracy advantage over single steam ConvNets. Spatial stream ConvNet can achieve an accuracy of 65.2%, by extracting important features, including texture, plume area, and pattern. Temporal stream, fed by the results of optical flow analysis, results in an accuracy of 58.3%. The integration of the two-stream ConvNets gives a combined accuracy of 77.6%. For future work, we will split the training and testing datasets in distinct ways in order to test the generalization of the algorithm for different leak sources. Several analytic metrics, including confusion matrix and visualization of key features, will be used to understand accuracy rates and occurrences of false positives. The quantification algorithm can help to find and fix super-emitters, and improve the cost-effectiveness of leak detection and repair programs.

Crosstalk elimination in the detection of dual-beam optical tweezers by spatial filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ott, Dino; Oddershede, Lene B., E-mail: oddershede@nbi.dk; Reihani, S. Nader S.

2014-05-15

In dual-beam optical tweezers, the accuracy of position and force measurements is often compromised by crosstalk between the two detected signals, this crosstalk leading to systematic and significant errors on the measured forces and distances. This is true both for dual-beam optical traps where the splitting of the two traps is done by polarization optics and for dual optical traps constructed by other methods, e.g., holographic tweezers. If the two traps are orthogonally polarized, most often crosstalk is minimized by inserting polarization optics in front of the detector; however, this method is not perfect because of the de-polarization of themore » trapping beam introduced by the required high numerical aperture optics. Here we present a simple and easy-to-implement method to efficiently eliminate crosstalk. The method is based on spatial filtering by simply inserting a pinhole at the correct position and is highly compatible with standard back focal plane photodiode based detection of position and force. Our spatial filtering method reduces crosstalk up to five times better than polarization filtering alone. The effectiveness is dependent on pinhole size and distance between the traps and is here quantified experimentally and reproduced by theoretical modeling. The method here proposed will improve the accuracy of force-distance measurements, e.g., of single molecules, performed by dual-beam optical traps and hence give much more scientific value for the experimental efforts.« less

Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991-2011

NASA Technical Reports Server (NTRS)

Carroll, Mark; Wooten, Margaret; DiMiceli, Charlene; Sohlberg, Robert; Kelly, Maureen

2016-01-01

The availability of a dense time series of satellite observations at moderate (30 m) spatial resolution is enabling unprecedented opportunities for understanding ecosystems around the world. A time series of data from Landsat was used to generate a series of three maps at decadal time step to show how surface water has changed from 1991 to 2011 in the high northern latitudes of North America. Previous attempts to characterize the change in surface water in this region have been limited in either spatial or temporal resolution, or both. This series of maps was generated for the NASA Arctic and Boreal Vulnerability Experiment (ABoVE), which began in fall 2015. These maps show a nominal extent of surface water by using multiple observations to make a single map for each time step. This increases the confidence that any detected changes are related to climate or ecosystem changes not simply caused by short duration weather events such as flood or drought. The methods and comparison to other contemporary maps of the region are presented here. Initial verification results indicate 96% producer accuracy and 54% user accuracy when compared to 2-m resolution World View-2 data. All water bodies that were omitted were one Landsat pixel or smaller, hence below detection limits of the instrument.

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cummins, Kenneth L.; Cummins, Kenneth L.; Blakeslee, Richard J.; Goodman, Steven J.

2012-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

PubMed Central

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-01-01

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction. PMID:28837096

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

PubMed

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-08-24

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

Accuracy and Spatial Variability in GPS Surveying for Landslide Mapping on Road Inventories at a Semi-Detailed Scale: the Case in Colombia

NASA Astrophysics Data System (ADS)

Murillo Feo, C. A.; Martnez Martinez, L. J.; Correa Muñoz, N. A.

2016-06-01

The accuracy of locating attributes on topographic surfaces when, using GPS in mountainous areas, is affected by obstacles to wave propagation. As part of this research on the semi-automatic detection of landslides, we evaluate the accuracy and spatial distribution of the horizontal error in GPS positioning in the tertiary road network of six municipalities located in mountainous areas in the department of Cauca, Colombia, using geo-referencing with GPS mapping equipment and static-fast and pseudo-kinematic methods. We obtained quality parameters for the GPS surveys with differential correction, using a post-processing method. The consolidated database underwent exploratory analyses to determine the statistical distribution, a multivariate analysis to establish relationships and partnerships between the variables, and an analysis of the spatial variability and calculus of accuracy, considering the effect of non-Gaussian distribution errors. The evaluation of the internal validity of the data provide metrics with a confidence level of 95% between 1.24 and 2.45 m in the static-fast mode and between 0.86 and 4.2 m in the pseudo-kinematic mode. The external validity had an absolute error of 4.69 m, indicating that this descriptor is more critical than precision. Based on the ASPRS standard, the scale obtained with the evaluated equipment was in the order of 1:20000, a level of detail expected in the landslide-mapping project. Modelling the spatial variability of the horizontal errors from the empirical semi-variogram analysis showed predictions errors close to the external validity of the devices.

The use of Sentinel-2 imagery for seagrass mapping: Kalloni Gulf (Lesvos Island, Greece) case study

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Charalampis Spondylidis, Spyridon; Papakonstantinou, Apostolos; Soulakellis, Nikolaos

2016-08-01

Seagrass meadows play a significant role in ecosystems by stabilizing sediment and improving water clarity, which enhances seagrass growing conditions. It is high on the priority of EU legislation to map and protect them. The traditional use of medium spatial resolution satellite imagery e.g. Landsat-8 (30m) is very useful for mapping seagrass meadows on a regional scale. However, the availability of Sentinel-2 data, the recent ESA's satellite with its payload Multi-Spectral Instrument (MSI) is expected to improve the mapping accuracy. MSI designed to improve coastline studies due to its enhanced spatial and spectral capabilities e.g. optical bands with 10m spatial resolution. The present work examines the quality of Sentinel-2 images for seagrass mapping, the ability of each band in detection and discrimination of different habitats and estimates the accuracy of seagrass mapping. After pre-processing steps, e.g. radiometric calibration and atmospheric correction, image classified into four classes. Classification classes included sub-bottom composition e.g. seagrass, soft bottom, and hard bottom. Concrete vectors describing the areas covered by seagrass extracted from the high-resolution satellite image and used as in situ measurements. The developed methodology applied in the Gulf of Kalloni, (Lesvos Island - Greece). Results showed that Sentinel-2 images can be robustly used for seagrass mapping due to their spatial resolution, band availability and radiometric accuracy.

Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery

NASA Astrophysics Data System (ADS)

Gaertner, Julie; Genovese, Vanessa Brooks; Potter, Christopher; Sewake, Kelvin; Manoukis, Nicholas C.

2017-10-01

Coffee is an important crop in tropical regions of the world; about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012.

Spatial Patterns of NLCD Land Cover Change Thematic Accuracy (2001 - 2011)

EPA Science Inventory

Research on spatial non-stationarity of land cover classification accuracy has been ongoing for over two decades. We extend the understanding of thematic map accuracy spatial patterns by: 1) quantifying spatial patterns of map-reference agreement for class-specific land cover c...

A method to detect layover and shadow based on distributed spaceborne single-baseline InSAR

NASA Astrophysics Data System (ADS)

Yun, Ren; Huanxin, Zou; Shilin, Zhou; Hao, Sun; Kefeng, Ji

2014-03-01

Layover and Shadow are inevitable phenomenena in InSAR, which seriously destroy the continuity of interferometric phase images and present difficulties in the follow-up phase unwrapping. Thus, it's significant to detect layover and shadow. This paper presents an approach to detect layover and shadow using the auto-correlation matrix and amplitude of the two images. The method can make full use of the spatial information of neighboring pixels and effectively detect layover and shadow regions in the case of low registration accuracy. Experiment result on the simulated data verifies effectiveness of the algorithm.

Characterization of nanoscale spatial distribution of small molecules in amorphous polymer matrices

NASA Astrophysics Data System (ADS)

Ricarte, Ralm; Hillmyer, Marc; Lodge, Timothy

Hydroxypropyl methylcellulose acetate succinate (HPMCAS) can significantly enhance the efficacy of active pharmaceutical ingredients (APIs). Yet, the interactions between species in HPMCAS-API blends are not understood. Elucidating these interactions is difficult because the spatial distributions of HPMCAS and API in the blends are ambiguous; the polymer and drug may be molecularly mixed or the species may form phase separated domains. As these phase separated domains may be less than 100 nm in size, traditional characterization techniques may not accurately evaluate the spatial distribution. To address this challenge, we explore the use of electron energy-loss spectroscopy (EELS) for detecting the model API phenytoin in an HPMCAS-phenytoin blend. Using EELS, we directly measured with high accuracy and precision the phenytoin concentrations in several blends. We present evidence that suggests phase separation occurs in blends that have a phenytoin loading of approximately 50 wt percent. Finally, we demonstrate that this technique achieves a sub-100 nm spatial resolution and can detect several other APIs.

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models

USGS Publications Warehouse

Sun, Catherine C.; Fuller, Angela K.; Royle, J. Andrew

2014-01-01

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

PubMed

Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

2017-06-14

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

A nonparametric spatial scan statistic for continuous data.

PubMed

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

Chandra ACIS Sub-pixel Resolution

NASA Astrophysics Data System (ADS)

Kim, Dong-Woo; Anderson, C. S.; Mossman, A. E.; Allen, G. E.; Fabbiano, G.; Glotfelty, K. J.; Karovska, M.; Kashyap, V. L.; McDowell, J. C.

2011-05-01

We investigate how to achieve the best possible ACIS spatial resolution by binning in ACIS sub-pixel and applying an event repositioning algorithm after removing pixel-randomization from the pipeline data. We quantitatively assess the improvement in spatial resolution by (1) measuring point source sizes and (2) detecting faint point sources. The size of a bright (but no pile-up), on-axis point source can be reduced by about 20-30%. With the improve resolution, we detect 20% more faint sources when embedded on the extended, diffuse emission in a crowded field. We further discuss the false source rate of about 10% among the newly detected sources, using a few ultra-deep observations. We also find that the new algorithm does not introduce a grid structure by an aliasing effect for dithered observations and does not worsen the positional accuracy

An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data.

PubMed

Wang, Zhi-Jie; Jiao, Ju-Ying; Lei, Bo; Su, Yuan

2015-09-01

Remote sensing can provide large-scale spatial data for the detection of vegetation types. In this study, two shortwave infrared spectral bands (TM5 and TM7) and one visible spectral band (TM3) of Landsat 5 TM data were used to detect five typical vegetation types (communities dominated by Bothriochloa ischaemum, Artemisia gmelinii, Hippophae rhamnoides, Robinia pseudoacacia, and Quercus liaotungensis) using 270 field survey data in the Yanhe watershed on the Loess Plateau. The relationships between 200 field data points and their corresponding radiance reflectance were analyzed, and the equation termed the vegetation type index (VTI) was generated. The VTI values of five vegetation types were calculated, and the accuracy was tested using the remaining 70 field data points. The applicability of VTI was also tested by the distribution of vegetation type of two small watersheds in the Yanhe watershed and field sample data collected from other regions (Ziwuling Region, Huangling County, and Luochuan County) on the Loess Plateau. The results showed that the VTI can effectively detect the five vegetation types with an average accuracy exceeding 80 % and a representativeness above 85 %. As a new approach for monitoring vegetation types using remote sensing at a larger regional scale, VTI can play an important role in the assessment of vegetation restoration and in the investigation of the spatial distribution and community diversity of vegetation on the Loess Plateau.

Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel - 1 Images

NASA Astrophysics Data System (ADS)

Roychowdhury, K.

2016-06-01

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI

PubMed Central

Reichert, Christoph; Dürschmid, Stefan; Heinze, Hans-Jochen; Hinrichs, Hermann

2017-01-01

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG. PMID:29085279

Spatial Lattice Modulation for MIMO Systems

NASA Astrophysics Data System (ADS)

Choi, Jiwook; Nam, Yunseo; Lee, Namyoon

2018-06-01

This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multipleinput-multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multi-dimensional signal set that consists oflattice points. One major finding is that SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods for the MIMO channel under the constraint ofM-ary pulseamplitude-modulation (PAM) input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio (SNR) gain, i.e., a nominal coding gain, is attainable compared to the existing methods. In addition, closed-form expressions for both the average mutual information and average symbol-vector-error-probability (ASVEP) of generic SLM are derived under Rayleigh-fading environments. To reduce detection complexity, a low-complexity detection method for SLM, which is referred to as lattice sphere decoding, is developed by exploiting lattice theory. Simulation results verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower ASVEP than do existing methods.

A hyperspectral imagery anomaly detection algorithm based on local three-dimensional orthogonal subspace projection

NASA Astrophysics Data System (ADS)

Zhang, Xing; Wen, Gongjian

2015-10-01

Anomaly detection (AD) becomes increasingly important in hyperspectral imagery analysis with many practical applications. Local orthogonal subspace projection (LOSP) detector is a popular anomaly detector which exploits local endmembers/eigenvectors around the pixel under test (PUT) to construct background subspace. However, this subspace only takes advantage of the spectral information, but the spatial correlat ion of the background clutter is neglected, which leads to the anomaly detection result sensitive to the accuracy of the estimated subspace. In this paper, a local three dimensional orthogonal subspace projection (3D-LOSP) algorithm is proposed. Firstly, under the jointly use of both spectral and spatial information, three directional background subspaces are created along the image height direction, the image width direction and the spectral direction, respectively. Then, the three corresponding orthogonal subspaces are calculated. After that, each vector along three direction of the local cube is projected onto the corresponding orthogonal subspace. Finally, a composite score is given through the three direction operators. In 3D-LOSP, the anomalies are redefined as the target not only spectrally different to the background, but also spatially distinct. Thanks to the addition of the spatial information, the robustness of the anomaly detection result has been improved greatly by the proposed 3D-LOSP algorithm. It is noteworthy that the proposed algorithm is an expansion of LOSP and this ideology can inspire many other spectral-based anomaly detection methods. Experiments with real hyperspectral images have proved the stability of the detection result.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Semantic Segmentation of Forest Stands of Pure Species as a Global Optimization Problem

NASA Astrophysics Data System (ADS)

Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V.

2017-05-01

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

PubMed Central

Raptis, Sotirios; Koutsouris, Dimitris

2010-01-01

The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection. PMID:20706682

Evaluating satellite imagery for estimating mountain pine beetle-caused lodgepole pine mortality: Current status

Treesearch

B. J. Bentz; D. Endreson

2004-01-01

Spatial accuracy in the detection and monitoring of mountain pine beetle populations is an important aspect of both forest research and management. Using ground-collected data, classification models to predict mountain pine beetle-caused lodgepole pine mortality were developed for Landsat TM, ETM+, and IKONOS imagery. Our results suggest that low-resolution imagery...

Robust object tracking techniques for vision-based 3D motion analysis applications

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

2016-04-01

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Fernandes, Maria Rosário; Aguiar, Francisca C.; Silva, João M. N.; Ferreira, Maria Teresa; Pereira, José M. C.

2014-10-01

Giant reed is an aggressive invasive plant of riparian ecosystems in many sub-tropical and warm-temperate regions, including Mediterranean Europe. In this study we tested a set of geometric, spectral and textural attributes in an object based image analysis (OBIA) approach to map giant reed invasions in riparian habitats. Bagging Classification and Regression Tree were used to select the optimal attributes and to build the classification rules sets. Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometric similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing. The methodology was applied in two high spatial resolution images: an airborne multispectral imagery and the newly WorldView-2 imagery. A temporal coverage of the airborne multispectral images was radiometrically calibrated with the IR-Mad transformation and used to assess the influence of the phenological variability of the invader. We found that optimal attributes for giant reed OBIA detection are a combination of spectral, geometric and textural information, with different scoring selection depending on the spectral and spatial characteristics of the imagery. WorldView-2 showed higher mapping accuracy (Kappa coefficient of 77%) and spectral attributes, including the newly yellow band, were preferentially selected, although a tendency to overestimate the total invaded area, due to the low spatial resolution (2 m of pixel size vs. 50 cm) was observed. When airborne images were used, geometric attributes were primarily selected and a higher spatial detail of the invasive patches was obtained, due to the higher spatial resolution. However, in highly heterogeneous landscapes, the low spectral resolution of the airborne images (4 bands instead of the 8 of WorldView-2) reduces the capability to detect giant reed patches. Giant reed displays peculiar spectral and geometric traits, at leaf, canopy and stand level, which makes the OBIA approach a very suitable technique for management purposes.

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

NASA Astrophysics Data System (ADS)

Hu, B.; Li, J.; Wang, J.; Hall, B.

2014-11-01

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Real-time detection and discrimination of visual perception using electrocorticographic signals

NASA Astrophysics Data System (ADS)

Kapeller, C.; Ogawa, H.; Schalk, G.; Kunii, N.; Coon, W. G.; Scharinger, J.; Guger, C.; Kamada, K.

2018-06-01

Objective. Several neuroimaging studies have demonstrated that the ventral temporal cortex contains specialized regions that process visual stimuli. This study investigated the spatial and temporal dynamics of electrocorticographic (ECoG) responses to different types and colors of visual stimulation that were presented to four human participants, and demonstrated a real-time decoder that detects and discriminates responses to untrained natural images. Approach. ECoG signals from the participants were recorded while they were shown colored and greyscale versions of seven types of visual stimuli (images of faces, objects, bodies, line drawings, digits, and kanji and hiragana characters), resulting in 14 classes for discrimination (experiment I). Additionally, a real-time system asynchronously classified ECoG responses to faces, kanji and black screens presented via a monitor (experiment II), or to natural scenes (i.e. the face of an experimenter, natural images of faces and kanji, and a mirror) (experiment III). Outcome measures in all experiments included the discrimination performance across types based on broadband γ activity. Main results. Experiment I demonstrated an offline classification accuracy of 72.9% when discriminating among the seven types (without color separation). Further discrimination of grey versus colored images reached an accuracy of 67.1%. Discriminating all colors and types (14 classes) yielded an accuracy of 52.1%. In experiment II and III, the real-time decoder correctly detected 73.7% responses to face, kanji and black computer stimuli and 74.8% responses to presented natural scenes. Significance. Seven different types and their color information (either grey or color) could be detected and discriminated using broadband γ activity. Discrimination performance maximized for combined spatial-temporal information. The discrimination of stimulus color information provided the first ECoG-based evidence for color-related population-level cortical broadband γ responses in humans. Stimulus categories can be detected by their ECoG responses in real time within 500 ms with respect to stimulus onset.

Human ear detection in the thermal infrared spectrum

NASA Astrophysics Data System (ADS)

Abaza, Ayman; Bourlai, Thirimachos

2012-06-01

In this paper the problem of human ear detection in the thermal infrared (IR) spectrum is studied in order to illustrate the advantages and limitations of the most important steps of ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible and thermal profile face images. The thermal data was collected using a high definition middle-wave infrared (3-5 microns) camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based ear detection method is developed for real-time segmentation of human ears in either day or night time environments. The proposed method is based on Haar features forming a cascaded AdaBoost classifier (our modified version of the original Viola-Jones approach1 that was designed to be applied mainly in visible band images). The main advantage of the proposed method, applied on our profile face image data set collected in the thermal-band, is that it is designed to reduce the learning time required by the original Viola-Jones method from several weeks to several hours. Unlike other approaches reported in the literature, which have been tested but not designed to operate in the thermal band, our method yields a high detection accuracy that reaches ~ 91.5%. Further analysis on our data set yielded that: (a) photometric normalization techniques do not directly improve ear detection performance. However, when using a certain photometric normalization technique (CLAHE) on falsely detected images, the detection rate improved by ~ 4%; (b) the high detection accuracy of our method did not degrade when we lowered down the original spatial resolution of thermal ear images. For example, even after using one third of the original spatial resolution (i.e. ~ 20% of the original computational time) of the thermal profile face images, the high ear detection accuracy of our method remained unaffected. This resulted also in speeding up the detection time of an ear image from 265 to 17 milliseconds per image. To the best of our knowledge this is the first time that the problem of human ear detection in the thermal band is being investigated in the open literature.

Signal detection evidence for limited capacity in visual search

PubMed Central

Fencsik, David E.; Flusberg, Stephen J.; Horowitz, Todd S.; Wolfe, Jeremy M.

2014-01-01

The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a “2” among “5”s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1–8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models. PMID:21901574

a Comparative Analysis of Five Cropland Datasets in Africa

NASA Astrophysics Data System (ADS)

Wei, Y.; Lu, M.; Wu, W.

2018-04-01

The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

EPA Science Inventory

The accuracy of thematic map products is not spatially homogenous, but instead variable across most landscapes. Properly analyzing and representing the spatial distribution (pattern) of thematic map accuracy would provide valuable user information for assessing appropriate applic...

A new method for ultrasound detection of interfacial position in gas-liquid two-phase flow.

PubMed

Coutinho, Fábio Rizental; Ofuchi, César Yutaka; de Arruda, Lúcia Valéria Ramos; Neves, Flávio; Morales, Rigoberto E M

2014-05-22

Ultrasonic measurement techniques for velocity estimation are currently widely used in fluid flow studies and applications. An accurate determination of interfacial position in gas-liquid two-phase flows is still an open problem. The quality of this information directly reflects on the accuracy of void fraction measurement, and it provides a means of discriminating velocity information of both phases. The algorithm known as Velocity Matched Spectrum (VM Spectrum) is a velocity estimator that stands out from other methods by returning a spectrum of velocities for each interrogated volume sample. Interface detection of free-rising bubbles in quiescent liquid presents some difficulties for interface detection due to abrupt changes in interface inclination. In this work a method based on velocity spectrum curve shape is used to generate a spatial-temporal mapping, which, after spatial filtering, yields an accurate contour of the air-water interface. It is shown that the proposed technique yields a RMS error between 1.71 and 3.39 and a probability of detection failure and false detection between 0.89% and 11.9% in determining the spatial-temporal gas-liquid interface position in the flow of free rising bubbles in stagnant liquid. This result is valid for both free path and with transducer emitting through a metallic plate or a Plexiglas pipe.

A New Method for Ultrasound Detection of Interfacial Position in Gas-Liquid Two-Phase Flow

PubMed Central

Coutinho, Fábio Rizental; Ofuchi, César Yutaka; de Arruda, Lúcia Valéria Ramos; Jr., Flávio Neves; Morales, Rigoberto E. M.

2014-01-01

Ultrasonic measurement techniques for velocity estimation are currently widely used in fluid flow studies and applications. An accurate determination of interfacial position in gas-liquid two-phase flows is still an open problem. The quality of this information directly reflects on the accuracy of void fraction measurement, and it provides a means of discriminating velocity information of both phases. The algorithm known as Velocity Matched Spectrum (VM Spectrum) is a velocity estimator that stands out from other methods by returning a spectrum of velocities for each interrogated volume sample. Interface detection of free-rising bubbles in quiescent liquid presents some difficulties for interface detection due to abrupt changes in interface inclination. In this work a method based on velocity spectrum curve shape is used to generate a spatial-temporal mapping, which, after spatial filtering, yields an accurate contour of the air-water interface. It is shown that the proposed technique yields a RMS error between 1.71 and 3.39 and a probability of detection failure and false detection between 0.89% and 11.9% in determining the spatial-temporal gas-liquid interface position in the flow of free rising bubbles in stagnant liquid. This result is valid for both free path and with transducer emitting through a metallic plate or a Plexiglas pipe. PMID:24858961

A comparison of LANDSAT TM to MSS imagery for detecting submerged aquatic vegetation in lower Chesapeake Bay

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1985-01-01

LANDSAT Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery generated simultaneously over Guinea Marsh, Virginia, are assessed in the ability to detect submerged aquatic, bottom-adhering plant canopies (SAV). An unsupervised clustering algorithm is applied to both image types and the resulting classifications compared to SAV distributions derived from color aerial photography. Class confidence and accuracy are first computed for all water areas and then only shallow areas where water depth is less than 6 feet. In both the TM and MSS imagery, masking water areas deeper than 6 ft. resulted in greater classification accuracy at confidence levels greater than 50%. Both systems perform poorly in detecting SAV with crown cover densities less than 70%. On the basis of the spectral resolution, radiometric sensitivity, and location of visible bands, TM imagery does not offer a significant advantage over MSS data for detecting SAV in Lower Chesapeake Bay. However, because the TM imagery represents a higher spatial resolution, smaller SAV canopies may be detected than is possible with MSS data.

Pixels, Blocks of Pixels, and Polygons: Choosing a Spatial Unit for Thematic Accuracy Assessment

EPA Science Inventory

Pixels, polygons, and blocks of pixels are all potentially viable spatial assessment units for conducting an accuracy assessment. We develop a statistical population-based framework to examine how the spatial unit chosen affects the outcome of an accuracy assessment. The populati...

Detection of proximal caries using digital radiographic systems with different resolutions.

PubMed

Nikneshan, Sima; Abbas, Fatemeh Mashhadi; Sabbagh, Sedigheh

2015-01-01

Dental radiography is an important tool for detection of caries and digital radiography is the latest advancement in this regard. Spatial resolution is a characteristic of digital receptors used for describing the quality of images. This study was aimed to compare the diagnostic accuracy of two digital radiographic systems with three different resolutions for detection of noncavitated proximal caries. Diagnostic accuracy. Seventy premolar teeth were mounted in 14 gypsum blocks. Digora; Optime and RVG Access were used for obtaining digital radiographs. Six observers evaluated the proximal surfaces in radiographs for each resolution in order to determine the depth of caries based on a 4-point scale. The teeth were then histologically sectioned, and the results of histologic analysis were considered as the gold standard. Data were entered using SPSS version 18 software and the Kruskal-Wallis test was used for data analysis. P <0.05 was considered as statistically significant. No significant difference was found between different resolutions for detection of proximal caries (P > 0.05). RVG access system had the highest specificity (87.7%) and Digora; Optime at high resolution had the lowest specificity (84.2%). Furthermore, Digora; Optime had higher sensitivity for detection of caries exceeding outer half of enamel. Judgment of oral radiologists for detection of the depth of caries had higher reliability than that of restorative dentistry specialists. The three resolutions of Digora; Optime and RVG access had similar accuracy in detection of noncavitated proximal caries.

Comparison of direct 13C and indirect 1H-[13C] MR detection methods for the study of dynamic metabolic turnover in the human brain

NASA Astrophysics Data System (ADS)

Chen, Hao; De Feyter, Henk M.; Brown, Peter B.; Rothman, Douglas L.; Cai, Shuhui; de Graaf, Robin A.

2017-10-01

A wide range of direct 13C and indirect 1H-[13C] MR detection methods exist to probe dynamic metabolic pathways in the human brain. Choosing an optimal detection method is difficult as sequence-specific features regarding spatial localization, broadband decoupling, spectral resolution, power requirements and sensitivity complicate a straightforward comparison. Here we combine density matrix simulations with experimentally determined values for intrinsic 1H and 13C sensitivity, T1 and T2 relaxation and transmit efficiency to allow selection of an optimal 13C MR detection method for a given application and magnetic field. The indirect proton-observed, carbon-edited (POCE) detection method provides the highest accuracy at reasonable RF power deposition both at 4 T and 7 T. The various polarization transfer methods all have comparable performances, but may become infeasible at 7 T due to the high RF power deposition. 2D MR methods have limited value for the metabolites considered (primarily glutamate, glutamine and γ-amino butyric acid (GABA)), but may prove valuable when additional information can be extracted, such as isotopomers or lipid composition. While providing the lowest accuracy, the detection of non-protonated carbons is the simplest to implement with the lowest RF power deposition. The magnetic field homogeneity is one of the most important parameters affecting the detection accuracy for all metabolites and all acquisition methods.

Automated thematic mapping and change detection of ERTS-A images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Accuracy assessment of high frequency 3D ultrasound for digital impression-taking of prepared teeth

NASA Astrophysics Data System (ADS)

Heger, Stefan; Vollborn, Thorsten; Tinschert, Joachim; Wolfart, Stefan; Radermacher, Klaus

2013-03-01

Silicone based impression-taking of prepared teeth followed by plaster casting is well-established but potentially less reliable, error-prone and inefficient, particularly in combination with emerging techniques like computer aided design and manufacturing (CAD/CAM) of dental prosthesis. Intra-oral optical scanners for digital impression-taking have been introduced but until now some drawbacks still exist. Because optical waves can hardly penetrate liquids or soft-tissues, sub-gingival preparations still need to be uncovered invasively prior to scanning. High frequency ultrasound (HFUS) based micro-scanning has been recently investigated as an alternative to optical intra-oral scanning. Ultrasound is less sensitive against oral fluids and in principal able to penetrate gingiva without invasively exposing of sub-gingival preparations. Nevertheless, spatial resolution as well as digitization accuracy of an ultrasound based micro-scanning system remains a critical parameter because the ultrasound wavelength in water-like media such as gingiva is typically smaller than that of optical waves. In this contribution, the in-vitro accuracy of ultrasound based micro-scanning for tooth geometry reconstruction is being investigated and compared to its extra-oral optical counterpart. In order to increase the spatial resolution of the system, 2nd harmonic frequencies from a mechanically driven focused single element transducer were separated and corresponding 3D surface models were calculated for both fundamentals and 2nd harmonics. Measurements on phantoms, model teeth and human teeth were carried out for evaluation of spatial resolution and surface detection accuracy. Comparison of optical and ultrasound digital impression taking indicate that, in terms of accuracy, ultrasound based tooth digitization can be an alternative for optical impression-taking.

Multiple sensor integration for seizure onset detection in human patients comparing conventional disc versus novel tripolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Ding, Quan; Martínez-Juárez, Iris E; Gaitanis, John; Kay, Steven M; Besio, Walter G

2013-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Closed-loop systems that apply electrical stimulation when seizure onset is automatically detected require high accuracy of automatic seizure detection based on electrographic brain activity. To improve this accuracy we propose to use noninvasive tripolar concentric ring electrodes that have been shown to have significantly better signal-to-noise ratio, spatial selectivity, and mutual information compared to conventional disc electrodes. The proposed detection methodology is based on integration of multiple sensors using exponentially embedded family (EEF). In this preliminary study it is validated on over 26.3 hours of data collected using both tripolar concentric ring and conventional disc electrodes concurrently each from 7 human patients with epilepsy including five seizures. For a cross-validation based group model EEF correctly detected 100% and 80% of seizures respectively with <0.76 and <1.56 false positive detections per hour respectively for the two electrode modalities. These results clearly suggest the potential of seizure onset detection based on data from tripolar concentric ring electrodes.

Noncontact blood species identification method based on spatially resolved near-infrared transmission spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Linna; Sun, Meixiu; Wang, Zhennan; Li, Hongxiao; Li, Yingxin; Li, Gang; Lin, Ling

2017-09-01

The inspection and identification of whole blood are crucially significant for import-export ports and inspection and quarantine departments. In our previous research, we proved Near-Infrared diffuse transmitted spectroscopy method was potential for noninvasively identifying three blood species, including macaque, human and mouse, with samples measured in the cuvettes. However, in open sampling cases, inspectors may be endangered by virulence factors in blood samples. In this paper, we explored the noncontact measurement for classification, with blood samples measured in the vacuum blood vessels. Spatially resolved near-infrared spectroscopy was used to improve the prediction accuracy. Results showed that the prediction accuracy of the model built with nine detection points was more than 90% in identification between all five species, including chicken, goat, macaque, pig and rat, far better than the performance of the model built with single-point spectra. The results fully supported the idea that spatially resolved near-infrared spectroscopy method can improve the prediction ability, and demonstrated the feasibility of this method for noncontact blood species identification in practical applications.

Development of a hemispherical rotational modulation collimator system for imaging spatial distribution of radiation sources

NASA Astrophysics Data System (ADS)

Na, M.; Lee, S.; Kim, G.; Kim, H. S.; Rho, J.; Ok, J. G.

2017-12-01

Detecting and mapping the spatial distribution of radioactive materials is of great importance for environmental and security issues. We design and present a novel hemispherical rotational modulation collimator (H-RMC) system which can visualize the location of the radiation source by collecting signals from incident rays that go through collimator masks. The H-RMC system comprises a servo motor-controlled rotating module and a hollow heavy-metallic hemisphere with slits/slats equally spaced with the same angle subtended from the main axis. In addition, we also designed an auxiliary instrument to test the imaging performance of the H-RMC system, comprising a high-precision x- and y-axis staging station on which one can mount radiation sources of various shapes. We fabricated the H-RMC system which can be operated in a fully-automated fashion through the computer-based controller, and verify the accuracy and reproducibility of the system by measuring the rotational and linear positions with respect to the programmed values. Our H-RMC system may provide a pivotal tool for spatial radiation imaging with high reliability and accuracy.

Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

NASA Astrophysics Data System (ADS)

Robinson, T. P.; Wardell-Johnson, G. W.; Pracilio, G.; Brown, C.; Corner, R.; van Klinken, R. D.

2016-02-01

Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71-0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m2. Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m2 allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management.

MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

EPA Science Inventory

This paper presents a fuzzy set-based method of mapping spatial accuracy of thematic map and computing several ecological indicators while taking into account spatial variation of accuracy associated with different land cover types and other factors (e.g., slope, soil type, etc.)...

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

X-Ray Microanalysis and Electron Energy Loss Spectrometry in the Analytical Electron Microscope: Review and Future Directions

NASA Technical Reports Server (NTRS)

Goldstein, J. I.; Williams, D. B.

1992-01-01

This paper reviews and discusses future directions in analytical electron microscopy for microchemical analysis using X-ray and Electron Energy Loss Spectroscopy (EELS). The technique of X-ray microanalysis, using the ratio method and k(sub AB) factors, is outlined. The X-ray absorption correction is the major barrier to the objective of obtaining I% accuracy and precision in analysis. Spatial resolution and Minimum Detectability Limits (MDL) are considered with present limitations of spatial resolution in the 2 to 3 microns range and of MDL in the 0.1 to 0.2 wt. % range when a Field Emission Gun (FEG) system is used. Future directions of X-ray analysis include improvement in X-ray spatial resolution to the I to 2 microns range and MDL as low as 0.01 wt. %. With these improvements the detection of single atoms in the analysis volume will be possible. Other future improvements include the use of clean room techniques for thin specimen preparation, quantification available at the I% accuracy and precision level with light element analysis quantification available at better than the 10% accuracy and precision level, the incorporation of a compact wavelength dispersive spectrometer to improve X-ray spectral resolution, light element analysis and MDL, and instrument improvements including source stability, on-line probe current measurements, stage stability, and computerized stage control. The paper reviews the EELS technique, recognizing that it has been slow to develop and still remains firmly in research laboratories rather than in applications laboratories. Consideration of microanalysis with core-loss edges is given along with a discussion of the limitations such as specimen thickness. Spatial resolution and MDL are considered, recognizing that single atom detection is already possible. Plasmon loss analysis is discussed as well as fine structure analysis. New techniques for energy-loss imaging are also summarized. Future directions in the EELS technique will be the development of new spectrometers and improvements in thin specimen preparation. The microanalysis technique needs to be simplified and software developed so that the EELS technique approaches the relative simplicity of the X-ray technique. Finally, one can expect major improvements in EELS imaging as data storage and processing improvements occur.

The Performance Analysis of a Uav Based Mobile Mapping System Platform

NASA Astrophysics Data System (ADS)

Tsai, M. L.; Chiang, K. W.; Lo, C. F.; Ch, C. H.

2013-08-01

To facilitate applications such as environment detection or disaster monitoring, the development of rapid low cost systems for collecting near real-time spatial information is very critical. Rapid spatial information collection has become an emerging trend for remote sensing and mapping applications. This study develops a Direct Georeferencing (DG) based fixed-wing Unmanned Aerial Vehicle (UAV) photogrammetric platform where an Inertial Navigation System (INS)/Global Positioning System (GPS) integrated Positioning and Orientation System (POS) system is implemented to provide the DG capability of the platform. The performance verification indicates that the proposed platform can capture aerial images successfully. A flight test is performed to verify the positioning accuracy in DG mode without using Ground Control Points (GCP). The preliminary results illustrate that horizontal DG positioning accuracies in the x and y axes are around 5 m with 300 m flight height. The positioning accuracy in the z axis is less than 10 m. Such accuracy is good for near real-time disaster relief. The DG ready function of proposed platform guarantees mapping and positioning capability even in GCP free environments, which is very important for rapid urgent response for disaster relief. Generally speaking, the data processing time for the DG module, including POS solution generalization, interpolation, Exterior Orientation Parameters (EOP) generation, and feature point measurements, is less than one hour.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Comparisons of neural networks to standard techniques for image classification and correlation

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1994-01-01

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

Identification of understory invasive exotic plants with remote sensing in urban forests

NASA Astrophysics Data System (ADS)

Shouse, Michael; Liang, Liang; Fei, Songlin

2013-04-01

Invasive exotic plants (IEP) pose a significant threat to many ecosystems. To effectively manage IEP, it is important to efficiently detect their presences and determine their distribution patterns. Remote sensing has been a useful tool to map IEP but its application is limited in urban forests, which are often the sources and sinks for IEP. In this study, we examined the feasibility and tradeoffs of species level IEP mapping using multiple remote sensing techniques in a highly complex urban forest setting. Bush honeysuckle (Lonicera maackii), a pervasive IEP in eastern North America, was used as our modeling species. Both medium spatial resolution (MSR) and high spatial resolution (HSR) imagery were employed in bush honeysuckle mapping. The importance of spatial scale was also examined using an up-scaling simulation from the HSR object based classification. Analysis using both MSR and HSR imagery provided viable results for IEP distribution mapping in urban forests. Overall mapping accuracy ranged from 89.8% to 94.9% for HSR techniques and from 74.6% to 79.7% for MSR techniques. As anticipated, classification accuracy reduces as pixel size increases. HSR based techniques produced the most desirable results, therefore is preferred for precise management of IEP in heterogeneous environment. However, the use of MSR techniques should not be ruled out given their wide availability and moderate accuracy.

Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

NASA Astrophysics Data System (ADS)

Chu, Zhigang; Yang, Yang; Shen, Linbang

2017-05-01

Functional delay and sum (FDAS) is a novel beamforming algorithm introduced for the three-dimensional (3D) acoustic source identification with solid spherical microphone arrays. Being capable of offering significantly attenuated sidelobes with a fast speed, the algorithm promises to play an important role in interior acoustic source identification. However, it presents some intrinsic imperfections, specifically poor spatial resolution and low quantification accuracy. This paper focuses on conquering these imperfections by ridge detection (RD) and deconvolution approach for the mapping of acoustic sources (DAMAS). The suggested methods are referred to as FDAS+RD and FDAS+RD+DAMAS. Both computer simulations and experiments are utilized to validate their effects. Several interesting conclusions have emerged: (1) FDAS+RD and FDAS+RD+DAMAS both can dramatically ameliorate FDAS's spatial resolution and at the same time inherit its advantages. (2) Compared to the conventional DAMAS, FDAS+RD+DAMAS enjoys the same super spatial resolution, stronger sidelobe attenuation capability and more than two hundred times faster speed. (3) FDAS+RD+DAMAS can effectively conquer FDAS's low quantification accuracy. Whether the focus distance is equal to the distance from the source to the array center or not, it can quantify the source average pressure contribution accurately. This study will be of great significance to the accurate and quick localization and quantification of acoustic sources in cabin environments.

Identification and delineation of areas flood hazard using high accuracy of DEM data

NASA Astrophysics Data System (ADS)

Riadi, B.; Barus, B.; Widiatmaka; Yanuar, M. J. P.; Pramudya, B.

2018-05-01

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

Mapping Tamarix: New techniques for field measurements, spatial modeling and remote sensing

NASA Astrophysics Data System (ADS)

Evangelista, Paul H.

Native riparian ecosystems throughout the southwestern United States are being altered by the rapid invasion of Tamarix species, commonly known as tamarisk. The effects that tamarisk has on ecosystem processes have been poorly quantified largely due to inadequate survey methods. I tested new approaches for field measurements, spatial models and remote sensing to improve our ability measure and to map tamarisk occurrence, and provide new methods that will assist in management and control efforts. Examining allometric relationships between basal cover and height measurements collected in the field, I was able to produce several models to accurately estimate aboveground biomass. The best two models were explained 97% of the variance (R 2 = 0.97). Next, I tested five commonly used predictive spatial models to identify which methods performed best for tamarisk using different types of data collected in the field. Most spatial models performed well for tamarisk, with logistic regression performing best with an Area Under the receiver-operating characteristic Curve (AUC) of 0.89 and overall accuracy of 85%. The results of this study also suggested that models may not perform equally with different invasive species, and that results may be influenced by species traits and their interaction with environmental factors. Lastly, I tested several approaches to improve the ability to remotely sense tamarisk occurrence. Using Landsat7 ETM+ satellite scenes and derived vegetation indices for six different months of the growing season, I examined their ability to detect tamarisk individually (single-scene analyses) and collectively (time-series). My results showed that time-series analyses were best suited to distinguish tamarisk from other vegetation and landscape features (AUC = 0.96, overall accuracy = 90%). June, August and September were the best months to detect unique phenological attributes that are likely related to the species' extended growing season and green-up during peak growing months. These studies demonstrate that new techniques can further our understanding of tamarisk's impacts on ecosystem processes, predict potential distribution and new invasions, and improve our ability to detect occurrence using remote sensing techniques. Collectively, the results of my studies may increase our ability to map tamarisk distributions and better quantify its impacts over multiple spatial and temporal scales.

Linear excitation and detection in Fourier transform ion cyclotron resonance mass spectrometry

NASA Astrophysics Data System (ADS)

Grosshans, Peter B.; Chen, Ruidan; Limbach, Patrick A.; Marshall, Alan G.

1994-11-01

We present the first Fourier transform ion cyclotron resonance (FT-ICR) ion trap designed to produce both a linear spatial variation of the excitation electric potential field and a linear response of the detection circuit to the motion of the confined ions. With this trap, the magnitude of the detected signal at a given ion cyclotron frequency varies linearly with both the number of ions of given mass-to-charge ratio and also with the magnitude-mode excitation signal at the ion cyclotron orbital frequency; the proportionality constant is mass independent. Interestingly, this linearization may be achieved with any ion trap geometry. The excitation/detection design consists of an array of capacitively coupled electrodes which provide a voltage-divider network that produces a nearly spatially homogeneous excitation electric field throughout the linearized trap; resistive coupling to the electrodes isolates the a.c. excitation (or detection) circuit from the d.c. (trapping) potential. The design is based on analytical expressions for the potential associated with each electrode, from which we are able to compute the deviation from linearity for a trap with a finite number of elements. Based on direct experimental comparisons to an unmodified cubic trap, the linearized trap demonstrates the following performance advantages at the cost of some additional mechanical complexity: (a) signal response linearly proportional to excitation electric field amplitude; (b) vastly reduced axial excitation/ejection for significantly improved ion relative abundance accuracy; (c) elimination of harmonics and sidebands of the fundamental frequencies of ion motion. As a result, FT-ICR mass spectra are now more reproducible. Moreover, the linearized trap should facilitate the characterization of other fundamental aspects of ion behavior in an ICR ion trap, e.g. effects of space charge, non-quadrupolar electrostatic trapping field, etc. Furthermore, this novel design should improve significantly the precision of ion relative abundance and mass accuracy measurements, while removing spectral artifacts of the detection process. We discuss future modifications that linearize the spatial variation of the electrostatic trapping electric field as well, thereby completing the linearization of the entire FT-ICR mass spectrometric techniques. Suggested FT-ICR mass spectrometric applications for the linearized trap are discussed.

Application of SEASAT-1 Synthetic Aperture Radar (SAR) data to enhance and detect geological lineaments and to assist LANDSAT landcover classification mapping. [Appalachian Region, West Virginia

NASA Technical Reports Server (NTRS)

Sekhon, R.

1981-01-01

Digital SEASAT-1 synthetic aperture radar (SAR) data were used to enhance linear features to extract geologically significant lineaments in the Appalachian region. Comparison of Lineaments thus mapped with an existing lineament map based on LANDSAT MSS images shows that appropriately processed SEASAT-1 SAR data can significantly improve the detection of lineaments. Merge MSS and SAR data sets were more useful fo lineament detection and landcover classification than LANDSAT or SEASAT data alone. About 20 percent of the lineaments plotted from the SEASAT SAR image did not appear on the LANDSAT image. About 6 percent of minor lineaments or parts of lineaments present in the LANDSAT map were missing from the SEASAT map. Improvement in the landcover classification (acreage and spatial estimation accuracy) was attained by using MSS-SAR merged data. The aerial estimation of residential/built-up and forest categories was improved. Accuracy in estimating the agricultural and water categories was slightly reduced.

ASSESSING THE ACCURACY OF NATIONAL LAND COVER DATASET AREA ESTIMATES AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Site specific accuracy assessments provide fine-scale evaluation of the thematic accuracy of land use/land cover (LULC) datasets; however, they provide little insight into LULC accuracy across varying spatial extents. Additionally, LULC data are typically used to describe lands...

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm.

PubMed

Wang, Xingmei; Liu, Shu; Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

PubMed Central

Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method. PMID:28542266

A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National Forest

Treesearch

Susan Hummel; A. T. Hudak; E. H. Uebler; M. J. Falkowski; K. A. Megown

2011-01-01

Foresters are increasingly interested in remote sensing data because they provide an overview of landscape conditions, which is impractical with field sample data alone. Light Detection and Ranging (LiDAR) provides exceptional spatial detail of forest structure, but difficulties in processing LiDAR data have limited their application beyond the research community....

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation. Conclusion High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage. PMID:24314148

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.

PubMed

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-09-09

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar's estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.

Contingent capture of involuntary visual attention interferes with detection of auditory stimuli

PubMed Central

Kamke, Marc R.; Harris, Jill

2014-01-01

The involuntary capture of attention by salient visual stimuli can be influenced by the behavioral goals of an observer. For example, when searching for a target item, irrelevant items that possess the target-defining characteristic capture attention more strongly than items not possessing that feature. Such contingent capture involves a shift of spatial attention toward the item with the target-defining characteristic. It is not clear, however, if the associated decrements in performance for detecting the target item are entirely due to involuntary orienting of spatial attention. To investigate whether contingent capture also involves a non-spatial interference, adult observers were presented with streams of visual and auditory stimuli and were tasked with simultaneously monitoring for targets in each modality. Visual and auditory targets could be preceded by a lateralized visual distractor that either did, or did not, possess the target-defining feature (a specific color). In agreement with the contingent capture hypothesis, target-colored distractors interfered with visual detection performance (response time and accuracy) more than distractors that did not possess the target color. Importantly, the same pattern of results was obtained for the auditory task: visual target-colored distractors interfered with sound detection. The decrement in auditory performance following a target-colored distractor suggests that contingent capture involves a source of processing interference in addition to that caused by a spatial shift of attention. Specifically, we argue that distractors possessing the target-defining characteristic enter a capacity-limited, serial stage of neural processing, which delays detection of subsequently presented stimuli regardless of the sensory modality. PMID:24920945

Contingent capture of involuntary visual attention interferes with detection of auditory stimuli.

PubMed

Kamke, Marc R; Harris, Jill

2014-01-01

The involuntary capture of attention by salient visual stimuli can be influenced by the behavioral goals of an observer. For example, when searching for a target item, irrelevant items that possess the target-defining characteristic capture attention more strongly than items not possessing that feature. Such contingent capture involves a shift of spatial attention toward the item with the target-defining characteristic. It is not clear, however, if the associated decrements in performance for detecting the target item are entirely due to involuntary orienting of spatial attention. To investigate whether contingent capture also involves a non-spatial interference, adult observers were presented with streams of visual and auditory stimuli and were tasked with simultaneously monitoring for targets in each modality. Visual and auditory targets could be preceded by a lateralized visual distractor that either did, or did not, possess the target-defining feature (a specific color). In agreement with the contingent capture hypothesis, target-colored distractors interfered with visual detection performance (response time and accuracy) more than distractors that did not possess the target color. Importantly, the same pattern of results was obtained for the auditory task: visual target-colored distractors interfered with sound detection. The decrement in auditory performance following a target-colored distractor suggests that contingent capture involves a source of processing interference in addition to that caused by a spatial shift of attention. Specifically, we argue that distractors possessing the target-defining characteristic enter a capacity-limited, serial stage of neural processing, which delays detection of subsequently presented stimuli regardless of the sensory modality.

Steganalysis using logistic regression

NASA Astrophysics Data System (ADS)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

High resolution mapping of development in the wildland-urban interface using object based image extraction.

PubMed

Caggiano, Michael D; Tinkham, Wade T; Hoffman, Chad; Cheng, Antony S; Hawbaker, Todd J

2016-10-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m 2 ) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

High resolution mapping of development in the wildland-urban interface using object based image extraction

USGS Publications Warehouse

Caggiano, Michael D.; Tinkham, Wade T.; Hoffman, Chad; Cheng, Antony S.; Hawbaker, Todd J.

2016-01-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

PubMed Central

Kanas, Vasileios G.; Mporas, Iosif; Benz, Heather L.; Sgarbas, Kyriakos N.; Bezerianos, Anastasios; Crone, Nathan E.

2014-01-01

Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication. PMID:24658248

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Structural damage detection using deep learning of ultrasonic guided waves

NASA Astrophysics Data System (ADS)

Melville, Joseph; Alguri, K. Supreet; Deemer, Chris; Harley, Joel B.

2018-04-01

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.

The Analysis of Burrows Recognition Accuracy in XINJIANG'S Pasture Area Based on Uav Visible Images with Different Spatial Resolution

NASA Astrophysics Data System (ADS)

Sun, D.; Zheng, J. H.; Ma, T.; Chen, J. J.; Li, X.

2018-04-01

The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80 %. At the altitude of 24 m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1 cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

Least Median of Squares Filtering of Locally Optimal Point Matches for Compressible Flow Image Registration

PubMed Central

Castillo, Edward; Castillo, Richard; White, Benjamin; Rojo, Javier; Guerrero, Thomas

2012-01-01

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. PMID:22797602

A New Moving Object Detection Method Based on Frame-difference and Background Subtraction

NASA Astrophysics Data System (ADS)

Guo, Jiajia; Wang, Junping; Bai, Ruixue; Zhang, Yao; Li, Yong

2017-09-01

Although many methods of moving object detection have been proposed, moving object extraction is still the core in video surveillance. However, with the complex scene in real world, false detection, missed detection and deficiencies resulting from cavities inside the body still exist. In order to solve the problem of incomplete detection for moving objects, a new moving object detection method combined an improved frame-difference and Gaussian mixture background subtraction is proposed in this paper. To make the moving object detection more complete and accurate, the image repair and morphological processing techniques which are spatial compensations are applied in the proposed method. Experimental results show that our method can effectively eliminate ghosts and noise and fill the cavities of the moving object. Compared to other four moving object detection methods which are GMM, VIBE, frame-difference and a literature's method, the proposed method improve the efficiency and accuracy of the detection.

Influence of the quality of intraoperative fluoroscopic images on the spatial positioning accuracy of a CAOS system.

PubMed

Wang, Junqiang; Wang, Yu; Zhu, Gang; Chen, Xiangqian; Zhao, Xiangrui; Qiao, Huiting; Fan, Yubo

2018-06-01

Spatial positioning accuracy is a key issue in a computer-assisted orthopaedic surgery (CAOS) system. Since intraoperative fluoroscopic images are one of the most important input data to the CAOS system, the quality of these images should have a significant influence on the accuracy of the CAOS system. But the regularities and mechanism of the influence of the quality of intraoperative images on the accuracy of a CAOS system have yet to be studied. Two typical spatial positioning methods - a C-arm calibration-based method and a bi-planar positioning method - are used to study the influence of different image quality parameters, such as resolution, distortion, contrast and signal-to-noise ratio, on positioning accuracy. The error propagation rules of image error in different spatial positioning methods are analyzed by the Monte Carlo method. Correlation analysis showed that resolution and distortion had a significant influence on spatial positioning accuracy. In addition the C-arm calibration-based method was more sensitive to image distortion, while the bi-planar positioning method was more susceptible to image resolution. The image contrast and signal-to-noise ratio have no significant influence on the spatial positioning accuracy. The result of Monte Carlo analysis proved that generally the bi-planar positioning method was more sensitive to image quality than the C-arm calibration-based method. The quality of intraoperative fluoroscopic images is a key issue in the spatial positioning accuracy of a CAOS system. Although the 2 typical positioning methods have very similar mathematical principles, they showed different sensitivities to different image quality parameters. The result of this research may help to create a realistic standard for intraoperative fluoroscopic images for CAOS systems. Copyright © 2018 John Wiley & Sons, Ltd.

Gradient field of undersea sound speed structure extracted from the GNSS-A oceanography

NASA Astrophysics Data System (ADS)

Yokota, Yusuke; Ishikawa, Tadashi; Watanabe, Shun-ichi

2018-06-01

After the twenty-first century, the Global Navigation Satellite System-Acoustic ranging (GNSS-A) technique detected geodetic events such as co- and postseismic effects following the 2011 Tohoku-oki earthquake and slip-deficit rate distributions along the Nankai Trough subduction zone. Although these are extremely important discoveries in geodesy and seismology, more accurate observation that can capture temporal and spatial changes are required for future earthquake disaster prevention. In order to upgrade the accuracy of the GNSS-A technique, it is necessary to understand disturbances in undersea sound speed structures, which are major error sources. In particular, detailed temporal and spatial variations are difficult to observe accurately, and their effect was not sufficiently extracted in previous studies. In the present paper, we reconstruct an inversion scheme for extracting the effect from GNSS-A data and experimentally apply this scheme to the seafloor sites around the Kuroshio. The extracted gradient effects are believed to represent not only a broad sound speed structure but also a more detailed structure generated in the unsteady disturbance. The accuracy of the seafloor positioning was also improved by this new method. The obtained results demonstrate the feasibility of using the GNSS-A technique to detect a seafloor crustal deformation for oceanography research.

4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging.

PubMed

Reilhac, Anthonin; Charil, Arnaud; Wimberley, Catriona; Angelis, Georgios; Hamze, Hasar; Callaghan, Paul; Garcia, Marie-Paule; Boisson, Frederic; Ryder, Will; Meikle, Steven R; Gregoire, Marie-Claude

2015-09-01

Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Using Electromagnetic Induction Technique to Detect Hydropedological Dynamics: Principles and Applications

NASA Astrophysics Data System (ADS)

Zhu, Qing; Liao, Kaihua; Doolittle, James; Lin, Henry

2014-05-01

Hydropedological dynamics including soil moisture variation, subsurface flow, and spatial distributions of different soil properties are important parameters in ecological, environmental, hydrological, and agricultural modeling and applications. However, technical gap exists in mapping these dynamics at intermediate spatial scale (e.g., farm and catchment scales). At intermediate scales, in-situ monitoring provides detailed data, but is restricted in number and spatial coverage; while remote sensing provides more acceptable spatial coverage, but has comparatively low spatial resolution, limited observation depths, and is greatly influenced by the surface condition and climate. As a non-invasive, fast, and convenient geophysical tool, electromagnetic induction (EMI) measures soil apparent electrical conductivity (ECa) and has great potential to bridge this technical gap. In this presentation, principles of different EMI meters are briefly introduced. Then, case studies of using repeated EMI to detect spatial distributions of subsurface convergent flow, soil moisture dynamics, soil types and their transition zones, and different soil properties are presented. The suitability, effectiveness, and accuracy of EMI are evaluated for mapping different hydropedological dynamics. Lastly, contributions of different hydropedological and terrain properties on soil ECa are quantified under different wetness conditions, seasons, and land use types using Classification and Regression Tree model. Trend removal and residual analysis are then used for further mining of EMI survey data. Based on these analyses, proper EMI survey designs and data processing are proposed.

Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy

NASA Astrophysics Data System (ADS)

Maspero, Matteo; van den Berg, Cornelis A. T.; Zijlstra, Frank; Sikkes, Gonda G.; de Boer, Hans C. J.; Meijer, Gert J.; Kerkmeijer, Linda G. W.; Viergever, Max A.; Lagendijk, Jan J. W.; Seevinck, Peter R.

2017-10-01

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of 1.1 × 1.1 × 1.2 mm3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a semi-automatic workflow facilitating the introduction of an MR-only workflow.

Technical Advances in Endoscopic Ultrasound (EUS)-Guided Tissue Acquisition for Pancreatic Cancers: How Can We Get the Best Results with EUS-Guided Fine Needle Aspiration?

PubMed Central

Kedia, Prashant; Gaidhane, Monica

2013-01-01

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is one of the least invasive and most effective modality in diagnosing pancreatic adenocarcinoma in solid pancreatic lesions, with a higher diagnostic accuracy than cystic tumors. EUS-FNA has been shown to detect tumors less than 3 mm, due to high spatial resolution allowing the detection of very small lesions and vascular invasion, particularly in the pancreatic head and neck, which may not be detected on transverse computed tomography. Furthermore, this minimally invasive procedure is often ideal in the endoscopic procurement of tissue in patients with unresectable tumors. While EUS-FNA has been increasingly used as a diagnostic tool, most studies have collectively looked at all primary pancreatic solid lesions, including lymphomas and pancreatic neuroendocrine neoplasms, whereas very few studies have examined the diagnostic utility of EUS-FNA of pancreatic ductal carcinoma only. As with any novel and advanced endoscopic procedure that may incorporate several practices and approaches, endoscopists have adopted diverse techniques to improve the tissue procurement practice and increase diagnostic accuracy. In this article, we present a review of literature to date and discuss currently practiced EUS-FNA technique, including indications, technical details, equipment, patient selection, and diagnostic accuracy. PMID:24143320

Effects of scale of movement, detection probability, and true population density on common methods of estimating population density

DOE PAGES

Keiter, David A.; Davis, Amy J.; Rhodes, Olin E.; ...

2017-08-25

Knowledge of population density is necessary for effective management and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ecological and observational processes, which can greatly influence accuracy and precision of density estimates. For this study, we simulate biological and observational processes using empirical data to assess effects of animal scale of movement, true population density, and probability of detection on common density estimators. We also apply common data collection and analytical techniques in the field and evaluate their ability to estimate density of a globally widespread species. We find that animal scale of movementmore » had the greatest impact on accuracy of estimators, although all estimators suffered reduced performance when detection probability was low, and we provide recommendations as to when each field and analytical technique is most appropriately employed. The large influence of scale of movement on estimator accuracy emphasizes the importance of effective post-hoc calculation of area sampled or use of methods that implicitly account for spatial variation. In particular, scale of movement impacted estimators substantially, such that area covered and spacing of detectors (e.g. cameras, traps, etc.) must reflect movement characteristics of the focal species to reduce bias in estimates of movement and thus density.« less

Effects of scale of movement, detection probability, and true population density on common methods of estimating population density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keiter, David A.; Davis, Amy J.; Rhodes, Olin E.

Knowledge of population density is necessary for effective management and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ecological and observational processes, which can greatly influence accuracy and precision of density estimates. For this study, we simulate biological and observational processes using empirical data to assess effects of animal scale of movement, true population density, and probability of detection on common density estimators. We also apply common data collection and analytical techniques in the field and evaluate their ability to estimate density of a globally widespread species. We find that animal scale of movementmore » had the greatest impact on accuracy of estimators, although all estimators suffered reduced performance when detection probability was low, and we provide recommendations as to when each field and analytical technique is most appropriately employed. The large influence of scale of movement on estimator accuracy emphasizes the importance of effective post-hoc calculation of area sampled or use of methods that implicitly account for spatial variation. In particular, scale of movement impacted estimators substantially, such that area covered and spacing of detectors (e.g. cameras, traps, etc.) must reflect movement characteristics of the focal species to reduce bias in estimates of movement and thus density.« less

Migrant deaths at the Arizona-Mexico border: Spatial trends of a mass disaster.

PubMed

Giordano, Alberto; Spradley, M Katherine

2017-11-01

Geographic Information Science (GIScience) technology has been used to document, investigate, and predict patterns that may be of utility in both forensic academic research and applied practice. In examining spatial and temporal trends of the mass disaster that is occurring along the U.S.-Mexico border, other researchers have highlighted predictive patterns for search and recovery efforts as well as water station placement. The purpose of this paper is to use previously collected spatial data of migrant deaths from Arizona to address issues of data uncertainty and data accuracy that affect our understanding of this phenomenon, including local and federal policies that impact the U.S.-Mexico border. The main objective of our study was to explore how the locations of migrant deaths have varied over time. Our results confirm patterns such as a lack of relationship between Border Patrol apprehensions and migrant deaths, as well as highlight new patterns such as the increased positional accuracy of migrant deaths recorded closer to the border. This paper highlights the importance of using positionally accurate data to detect spatio-temporal trends in forensic investigations of mass disasters: without qualitative and quantitative information concerning the accuracy of the data collected, the reliability of the results obtained remains questionable. We conclude by providing a set of guidelines for standardizing the collection and documentation of migrant remains at the U.S.-Mexico border. Copyright © 2017 Elsevier B.V. All rights reserved.

Follow your nose: Implicit spatial processing within the chemosensory systems.

PubMed

Wudarczyk, Olga A; Habel, Ute; Turetsky, Bruce I; Gur, Raquel E; Kellermann, Thilo; Schneider, Frank; Moessnang, Carolin

2016-11-01

Although most studies agree that humans cannot smell in stereo, it was recently suggested that olfactory localization is possible when assessed implicitly. In a spatial cueing paradigm, lateralized olfactory cues impaired the detection of congruently presented visual targets, an effect contrary to the typical facilitation observed in other sensory domains. Here, we examined the specificity and the robustness of this finding by studying implicit localization abilities in another chemosensory system and by accounting for possible confounds in a modified paradigm. Sixty participants completed a spatial cueing task along with an explicit localization task, using trigeminal (Experiment 1) and olfactory (Experiment 2) stimuli. A control task was implemented to control for residual somatosensory stimulation (Experiment 3). In the trigeminal experiment, stimuli were localized with high accuracy on the explicit level, while the cueing effect in form of facilitation was limited to response accuracy. In the olfactory experiment, responses were slowed by congruent cues on the implicit level, while no explicit localization was observed. Our results point to the robustness of the olfactory interference effect, corroborating the implicit-explicit dissociation of olfactory localization, and challenging the view that humans lost the ability to extract spatial information from smell. The absence of a similar interference for trigeminal cues suggests distinct implicit spatial processing mechanisms within the chemosensory systems. Moreover, the lack of a typical facilitation effect in the trigeminal domain points to important differences from spatial information processing in other, nonchemosensory domains. The possible mechanisms driving the effects are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

An analytic approach to the relation between GPS attitude determination accuracy and antenna configuration geometry

NASA Astrophysics Data System (ADS)

Kozlov, Alexander; Nikulin, Alexei

2017-01-01

The reliability and accuracy of GPS attitude determination are still the main relevant theoretical questions in this particular field of study. While the first one derives from the probabilistic nature of phase ambiguity resolution algorithms, outlier measurement detection and effectiveness of multipath reduction, the second is additionally affected by geometric properties of the GNSS antenna configuration. Being trivial in two-antenna system, the relation between GPS attitude determination accuracy and antenna spatial layout becomes much less intuitive for multi-antenna configurations, and seems to have been examined analytically in some specific cases only. For example, most of research papers in the field use Euler angles as attitude representation, which have singularity in some cases, and consider the number of antennas of not more than four. We present some further investigation in this area.

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

PubMed Central

Jeong, Myeong-Hun; Duckham, Matt

2015-01-01

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.

PubMed

Jeong, Myeong-Hun; Duckham, Matt

2015-08-28

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.

Double-Referential Holography and Spatial Quadrature Amplitude Modulation

NASA Astrophysics Data System (ADS)

Zukeran, Keisuke; Okamoto, Atsushi; Takabayashi, Masanori; Shibukawa, Atsushi; Sato, Kunihiro; Tomita, Akihisa

2013-09-01

We proposed a double-referential holography (DRH) that allows phase-detection without external additional beams. In the DRH, phantom beams, prepared in the same optical path as signal beams and preliminary multiplexed in a recording medium along with the signal, are used to produce interference fringes on an imager for converting a phase into an intensity distribution. The DRH enables stable and high-accuracy phase detection independent of the fluctuations and vibrations of the optical system owing to medium shift and temperature variation. Besides, the collinear arrangement of the signal and phantom beams leads to the compactness of the optical data storage system. We conducted an experiment using binary phase modulation signals for verifying the DRH operation. In addition, 38-level spatial quadrature amplitude modulation signals were successfully reproduced with the DRH by numerical simulation. Furthermore, we verified that the distributed phase-shifting method moderates the dynamic range consumption for the exposure of phantom beams.

Habitat complexity and fish size affect the detection of Indo-Pacific lionfish on invaded coral reefs

NASA Astrophysics Data System (ADS)

Green, S. J.; Tamburello, N.; Miller, S. E.; Akins, J. L.; Côté, I. M.

2013-06-01

A standard approach to improving the accuracy of reef fish population estimates derived from underwater visual censuses (UVCs) is the application of species-specific correction factors, which assumes that a species' detectability is constant under all conditions. To test this assumption, we quantified detection rates for invasive Indo-Pacific lionfish ( Pterois volitans and P. miles), which are now a primary threat to coral reef conservation throughout the Caribbean. Estimates of lionfish population density and distribution, which are essential for managing the invasion, are currently obtained through standard UVCs. Using two conventional UVC methods, the belt transect and stationary visual census (SVC), we assessed how lionfish detection rates vary with lionfish body size and habitat complexity (measured as rugosity) on invaded continuous and patch reefs off Cape Eleuthera, the Bahamas. Belt transect and SVC surveys performed equally poorly, with both methods failing to detect the presence of lionfish in >50 % of surveys where thorough, lionfish-focussed searches yielded one or more individuals. Conventional methods underestimated lionfish biomass by ~200 %. Crucially, detection rate varied significantly with both lionfish size and reef rugosity, indicating that the application of a single correction factor across habitats and stages of invasion is unlikely to accurately characterize local populations. Applying variable correction factors that account for site-specific lionfish size and rugosity to conventional survey data increased estimates of lionfish biomass, but these remained significantly lower than actual biomass. To increase the accuracy and reliability of estimates of lionfish density and distribution, monitoring programs should use detailed area searches rather than standard visual survey methods. Our study highlights the importance of accounting for sources of spatial and temporal variation in detection to increase the accuracy of survey data from coral reef systems.

Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States

USGS Publications Warehouse

Jin, Suming; Homer, Collin G.; Yang, Limin; Xian, George; Fry, Joyce; Danielson, Patrick; Townsend, Philip A.

2013-01-01

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

Detecting cm-scale hot spot over 24-km-long single-mode fiber by using differential pulse pair BOTDA based on double-peak spectrum.

PubMed

Diakaridia, Sanogo; Pan, Yue; Xu, Pengbai; Zhou, Dengwang; Wang, Benzhang; Teng, Lei; Lu, Zhiwei; Ba, Dexin; Dong, Yongkang

2017-07-24

In distributed Brillouin optical fiber sensor when the length of the perturbation to be detected is much smaller than the spatial resolution that is defined by the pulse width, the measured Brillouin gain spectrum (BGS) experiences two or multiple peaks. In this work, we propose and demonstrate a technique using differential pulse pair Brillouin optical time-domain analysis (DPP-BOTDA) based on double-peak BGS to enhance small-scale events detection capability, where two types of single mode fiber (main fiber and secondary fiber) with 116 MHz Brillouin frequency shift (BFS) difference have been used. We have realized detection of a 5-cm hot spot at the far end of 24-km single mode fiber by employing a 50-cm spatial resolution DPP-BOTDA with only 1GS/s sampling rate (corresponding to 10 cm/point). The BFS at the far end of 24-km sensing fiber has been measured with 0.54 MHz standard deviation which corresponds to a 0.5°C temperature accuracy. This technique is simple and cost effective because it is implemented using the similar experimental setup of the standard BOTDA, however, it should be noted that the consecutive small-scale events have to be separated by a minimum length corresponding to the spatial resolution defined by the pulse width difference.

Objective-lens-free Fiber-based Position Detection with Nanometer Resolution in a Fiber Optical Trapping System.

PubMed

Ti, Chaoyang; Ho-Thanh, Minh-Tri; Wen, Qi; Liu, Yuxiang

2017-10-13

Position detection with high accuracy is crucial for force calibration of optical trapping systems. Most existing position detection methods require high-numerical-aperture objective lenses, which are bulky, expensive, and difficult to miniaturize. Here, we report an affordable objective-lens-free, fiber-based position detection scheme with 2 nm spatial resolution and 150 MHz bandwidth. This fiber based detection mechanism enables simultaneous trapping and force measurements in a compact fiber optical tweezers system. In addition, we achieved more reliable signal acquisition with less distortion compared with objective based position detection methods, thanks to the light guiding in optical fibers and small distance between the fiber tips and trapped particle. As a demonstration of the fiber based detection, we used the fiber optical tweezers to apply a force on a cell membrane and simultaneously measure the cellular response.

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

1976-01-01

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Investigation of the interpolation method to improve the distributed strain measurement accuracy in optical frequency domain reflectometry systems.

PubMed

Cui, Jiwen; Zhao, Shiyuan; Yang, Di; Ding, Zhenyang

2018-02-20

We use a spectrum interpolation technique to improve the distributed strain measurement accuracy in a Rayleigh-scatter-based optical frequency domain reflectometry sensing system. We demonstrate that strain accuracy is not limited by the "uncertainty principle" that exists in the time-frequency analysis. Different interpolation methods are investigated and used to improve the accuracy of peak position of the cross-correlation and, therefore, improve the accuracy of the strain. Interpolation implemented by padding zeros on one side of the windowed data in the spatial domain, before the inverse fast Fourier transform, is found to have the best accuracy. Using this method, the strain accuracy and resolution are both improved without decreasing the spatial resolution. The strain of 3 μϵ within the spatial resolution of 1 cm at the position of 21.4 m is distinguished, and the measurement uncertainty is 3.3 μϵ.

The space-math link in preschool boys and girls: Importance of mental transformation, targeting accuracy, and spatial anxiety.

PubMed

Wong, Wang I

2017-06-01

Spatial abilities are pertinent to mathematical competence, but evidence of the space-math link has largely been confined to older samples and intrinsic spatial abilities (e.g., mental transformation). The roles of gender and affective factors are also unclear. This study examined the correlations between counting ability, mental transformation, and targeting accuracy in 182 Hong Kong preschoolers, and whether these relationships were weaker at higher spatial anxiety levels. Both spatial abilities related with counting similarly for boys and girls. Targeting accuracy also mediated the male advantage in counting. Interestingly, spatial anxiety moderated the space-math links, but differently for boys and girls. For boys, spatial abilities were irrelevant to counting at high anxiety levels; for girls, the role of anxiety on the space-math link is less clear. Results extend the evidence base of the space-math link to include an extrinsic spatial ability (targeting accuracy) and have implications for intervention programmes. Statement of contribution What is already known on this subject? Much evidence of a space-math link in adolescent and adult samples and for intrinsic spatial abilities. What does this study add? Extended the space-math link to include both intrinsic and extrinsic spatial abilities in a preschool sample. Showed how spatial anxiety moderated the space-math link differently for boys and girls. © 2016 The British Psychological Society.

Double peak-induced distance error in short-time-Fourier-transform-Brillouin optical time domain reflectometers event detection and the recovery method.

PubMed

Yu, Yifei; Luo, Linqing; Li, Bo; Guo, Linfeng; Yan, Jize; Soga, Kenichi

2015-10-01

The measured distance error caused by double peaks in the BOTDRs (Brillouin optical time domain reflectometers) system is a kind of Brillouin scattering spectrum (BSS) deformation, discussed and simulated for the first time in the paper, to the best of the authors' knowledge. Double peak, as a kind of Brillouin spectrum deformation, is important in the enhancement of spatial resolution, measurement accuracy, and crack detection. Due to the variances of the peak powers of the BSS along the fiber, the measured starting point of a step-shape frequency transition region is shifted and results in distance errors. Zero-padded short-time-Fourier-transform (STFT) can restore the transition-induced double peaks in the asymmetric and deformed BSS, thus offering more accurate and quicker measurements than the conventional Lorentz-fitting method. The recovering method based on the double-peak detection and corresponding BSS deformation can be applied to calculate the real starting point, which can improve the distance accuracy of the STFT-based BOTDR system.

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

PubMed Central

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-01-01

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. PMID:27618058

Role of interoceptive accuracy in topographical changes in emotion-induced bodily sensations

PubMed Central

Jung, Won-Mo; Ryu, Yeonhee; Lee, Ye-Seul; Wallraven, Christian; Chae, Younbyoung

2017-01-01

The emotion-associated bodily sensation map is composed of a specific topographical distribution of bodily sensations to categorical emotions. The present study investigated whether or not interoceptive accuracy was associated with topographical changes in this map following emotion-induced bodily sensations. This study included 31 participants who observed short video clips containing emotional stimuli and then reported their sensations on the body map. Interoceptive accuracy was evaluated with a heartbeat detection task and the spatial patterns of bodily sensations to specific emotions, including anger, fear, disgust, happiness, sadness, and neutral, were visualized using Statistical Parametric Mapping (SPM) analyses. Distinct patterns of bodily sensations were identified for different emotional states. In addition, positive correlations were found between the magnitude of sensation in emotion-specific regions and interoceptive accuracy across individuals. A greater degree of interoceptive accuracy was associated with more specific topographical changes after emotional stimuli. These results suggest that the awareness of one’s internal bodily states might play a crucial role as a required messenger of sensory information during the affective process. PMID:28877218

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

PubMed Central

Protopapadakis, Eftychios; Doulamis, Anastasios; Doulamis, Nikolaos; Dres, Dimitrios; Bimpas, Matthaios

2017-01-01

Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology's performance. PMID:29312449

Detection of increased vasa vasorum in artery walls: improving CT number accuracy using image deconvolution

NASA Astrophysics Data System (ADS)

Rajendran, Kishore; Leng, Shuai; Jorgensen, Steven M.; Abdurakhimova, Dilbar; Ritman, Erik L.; McCollough, Cynthia H.

2017-03-01

Changes in arterial wall perfusion are an indicator of early atherosclerosis. This is characterized by an increased spatial density of vasa vasorum (VV), the micro-vessels that supply oxygen and nutrients to the arterial wall. Detection of increased VV during contrast-enhanced computed tomography (CT) imaging is limited due to contamination from blooming effect from the contrast-enhanced lumen. We report the application of an image deconvolution technique using a measured system point-spread function, on CT data obtained from a photon-counting CT system to reduce blooming and to improve the CT number accuracy of arterial wall, which enhances detection of increased VV. A phantom study was performed to assess the accuracy of the deconvolution technique. A porcine model was created with enhanced VV in one carotid artery; the other carotid artery served as a control. CT images at an energy range of 25-120 keV were reconstructed. CT numbers were measured for multiple locations in the carotid walls and for multiple time points, pre and post contrast injection. The mean CT number in the carotid wall was compared between the left (increased VV) and right (control) carotid arteries. Prior to deconvolution, results showed similar mean CT numbers in the left and right carotid wall due to the contamination from blooming effect, limiting the detection of increased VV in the left carotid artery. After deconvolution, the mean CT number difference between the left and right carotid arteries was substantially increased at all the time points, enabling detection of the increased VV in the artery wall.

Optical filter highlighting spectral features part II: quantitative measurements of cosmetic foundation and assessment of their spatial distributions under realistic facial conditions.

PubMed

Nishino, Ken; Nakamura, Mutsuko; Matsumoto, Masayuki; Tanno, Osamu; Nakauchi, Shigeki

2011-03-28

We previously proposed a filter that could detect cosmetic foundations with high discrimination accuracy [Opt. Express 19, 6020 (2011)]. This study extends the filter's functionality to the quantification of the amount of foundation and applies the filter for the assessment of spatial distributions of foundation under realistic facial conditions. Human faces that are applied with quantitatively controlled amounts of cosmetic foundations were measured using the filter. A calibration curve between pixel values of the image and the amount of foundation was created. The optical filter was applied to visualize spatial foundation distributions under realistic facial conditions, which clearly indicated areas on the face where foundation remained even after cleansing. Results confirm that the proposed filter could visualize and nondestructively inspect the foundation distributions.

The basis function approach for modeling autocorrelation in ecological data

USGS Publications Warehouse

Hefley, Trevor J.; Broms, Kristin M.; Brost, Brian M.; Buderman, Frances E.; Kay, Shannon L.; Scharf, Henry; Tipton, John; Williams, Perry J.; Hooten, Mevin B.

2017-01-01

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

PubMed

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic.

PubMed

Read, S; Bath, P A; Willett, P; Maheswaran, R

2013-08-30

The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case-control study. A recent study suggests the inferential accuracy of several versions of the spatial scan statistic (principally the Poisson version) can be improved, at little computational cost, by using the Gumbel distribution, a method now available in SaTScan(TM) (www.satscan.org). We study in detail the effect of this technique when applied to the Bernoulli version and demonstrate that it is highly effective, albeit with some increase in false alarm rates at certain significance thresholds. We explain how this increase is due to the discrete nature of the Bernoulli spatial scan statistic and demonstrate that it can affect even small p-values. Despite this, we argue that the Gumbel method is actually preferable for very small p-values. Furthermore, we extend previous research by running benchmark trials on 12 000 synthetic datasets, thus demonstrating that the overall detection capability of the Bernoulli version (i.e. ratio of power to false alarm rate) is not noticeably affected by the use of the Gumbel method. We also provide an example application of the Gumbel method using data on hospital admissions for chronic obstructive pulmonary disease. Copyright © 2013 John Wiley & Sons, Ltd.

Spatial frequency characteristics at image decision-point locations for observers with different radiological backgrounds in lung nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Manning, David J.; Dix, Alan; Donovan, Tim

2009-02-01

Aim: The goal of the study is to determine the spatial frequency characteristics at locations in the image of overt and covert observers' decisions and find out if there are any similarities in different observers' groups: the same radiological experience group or the same accuracy scored level. Background: The radiological task is described as a visual searching decision making procedure involving visual perception and cognitive processing. Humans perceive the world through a number of spatial frequency channels, each sensitive to visual information carried by different spatial frequency ranges and orientations. Recent studies have shown that particular physical properties of local and global image-based elements are correlated with the performance and the level of experience of human observers in breast cancer and lung nodule detections. Neurological findings in visual perception were an inspiration for wavelet applications in vision research because the methodology tries to mimic the brain processing algorithms. Methods: The wavelet approach to the set of postero-anterior chest radiographs analysis has been used to characterize perceptual preferences observers with different levels of experience in the radiological task. Psychophysical methodology has been applied to track eye movements over the image, where particular ROIs related to the observers' fixation clusters has been analysed in the spaces frame by Daubechies functions. Results: Significance differences have been found between the spatial frequency characteristics at the location of different decisions.

Spatial and temporal task characteristics as stress: a test of the dynamic adaptability theory of stress, workload, and performance.

PubMed

Szalma, James L; Teo, Grace W L

2012-03-01

The goal for this study was to test assertions of the dynamic adaptability theory of stress, which proposes two fundamental task dimensions, information rate (temporal properties of a task) and information structure (spatial properties of a task). The theory predicts adaptive stability across stress magnitudes, with progressive and precipitous changes in adaptive response manifesting first as increases in perceived workload and stress and then as performance failure. Information structure was manipulated by varying the number of displays to be monitored (1, 2, 4 or 8 displays). Information rate was manipulated by varying stimulus presentation rate (8, 12, 16, or 20 events/min). A signal detection task was used in which critical signals were pairs of digits that differed by 0 or 1. Performance accuracy declined and workload and stress increased as a function of increased task demand, with a precipitous decline in accuracy at the highest demand levels. However, the form of performance change as well as the pattern of relationships between speed and accuracy and between performance and workload/stress indicates that some aspects of the theory need revision. Implications of the results for the theory and for future research are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

PHASE QUANTIZATION STUDY OF SPATIAL LIGHT MODULATOR FOR EXTREME HIGH-CONTRAST IMAGING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dou, Jiangpei; Ren, Deqing, E-mail: jpdou@niaot.ac.cn, E-mail: jiangpeidou@gmail.com

2016-11-20

Direct imaging of exoplanets by reflected starlight is extremely challenging due to the large luminosity ratio to the primary star. Wave-front control is a critical technique to attenuate the speckle noise in order to achieve an extremely high contrast. We present a phase quantization study of a spatial light modulator (SLM) for wave-front control to meet the contrast requirement of detection of a terrestrial planet in the habitable zone of a solar-type star. We perform the numerical simulation by employing the SLM with different phase accuracy and actuator numbers, which are related to the achievable contrast. We use an optimizationmore » algorithm to solve the quantization problems that is matched to the controllable phase step of the SLM. Two optical configurations are discussed with the SLM located before and after the coronagraph focal plane mask. The simulation result has constrained the specification for SLM phase accuracy in the above two optical configurations, which gives us a phase accuracy of 0.4/1000 and 1/1000 waves to achieve a contrast of 10{sup -10}. Finally, we have demonstrated that an SLM with more actuators can deliver a competitive contrast performance on the order of 10{sup -10} in comparison to that by using a deformable mirror.« less

Spatially-Resolved Hydraulic Conductivity Estimation Via Poroelastic Magnetic Resonance Elastography

PubMed Central

McGarry, Matthew; Weaver, John B.; Paulsen, Keith D.

2015-01-01

Poroelastic magnetic resonance elastography is an imaging technique that could recover mechanical and hydrodynamical material properties of in vivo tissue. To date, mechanical properties have been estimated while hydrodynamical parameters have been assumed homogeneous with literature-based values. Estimating spatially-varying hydraulic conductivity would likely improve model accuracy and provide new image information related to a tissue’s interstitial fluid compartment. A poroelastic model was reformulated to recover hydraulic conductivity with more appropriate fluid-flow boundary conditions. Simulated and physical experiments were conducted to evaluate the accuracy and stability of the inversion algorithm. Simulations were accurate (property errors were < 2%) even in the presence of Gaussian measurement noise up to 3%. The reformulated model significantly decreased variation in the shear modulus estimate (p≪0.001) and eliminated the homogeneity assumption and the need to assign hydraulic conductivity values from literature. Material property contrast was recovered experimentally in three different tofu phantoms and the accuracy was improved through soft-prior regularization. A frequency-dependence in hydraulic conductivity contrast was observed suggesting that fluid-solid interactions may be more prominent at low frequency. In vivo recovery of both structural and hydrodynamical characteristics of tissue could improve detection and diagnosis of neurological disorders such as hydrocephalus and brain tumors. PMID:24771571

Phase Quantization Study of Spatial Light Modulator for Extreme High-contrast Imaging

NASA Astrophysics Data System (ADS)

Dou, Jiangpei; Ren, Deqing

2016-11-01

Direct imaging of exoplanets by reflected starlight is extremely challenging due to the large luminosity ratio to the primary star. Wave-front control is a critical technique to attenuate the speckle noise in order to achieve an extremely high contrast. We present a phase quantization study of a spatial light modulator (SLM) for wave-front control to meet the contrast requirement of detection of a terrestrial planet in the habitable zone of a solar-type star. We perform the numerical simulation by employing the SLM with different phase accuracy and actuator numbers, which are related to the achievable contrast. We use an optimization algorithm to solve the quantization problems that is matched to the controllable phase step of the SLM. Two optical configurations are discussed with the SLM located before and after the coronagraph focal plane mask. The simulation result has constrained the specification for SLM phase accuracy in the above two optical configurations, which gives us a phase accuracy of 0.4/1000 and 1/1000 waves to achieve a contrast of 10-10. Finally, we have demonstrated that an SLM with more actuators can deliver a competitive contrast performance on the order of 10-10 in comparison to that by using a deformable mirror.

Cohen's Kappa and classification table metrics 2.0: An ArcView 3.x extension for accuracy assessment of spatially explicit models

Treesearch

Jeff Jenness; J. Judson Wynne

2005-01-01

In the field of spatially explicit modeling, well-developed accuracy assessment methodologies are often poorly applied. Deriving model accuracy metrics have been possible for decades, but these calculations were made by hand or with the use of a spreadsheet application. Accuracy assessments may be useful for: (1) ascertaining the quality of a model; (2) improving model...

Advanced Geospatial Hydrodynamic Signals Analysis for Tsunami Event Detection and Warning

NASA Astrophysics Data System (ADS)

Arbab-Zavar, Banafshe; Sabeur, Zoheir

2013-04-01

Current early tsunami warning can be issued upon the detection of a seismic event which may occur at a given location offshore. This also provides an opportunity to predict the tsunami wave propagation and run-ups at potentially affected coastal zones by selecting the best matching seismic event from a database of pre-computed tsunami scenarios. Nevertheless, it remains difficult and challenging to obtain the rupture parameters of the tsunamigenic earthquakes in real time and simulate the tsunami propagation with high accuracy. In this study, we propose a supporting approach, in which the hydrodynamic signal is systematically analysed for traces of a tsunamigenic signal. The combination of relatively low amplitudes of a tsunami signal at deep waters and the frequent occurrence of background signals and noise contributes to a generally low signal to noise ratio for the tsunami signal; which in turn makes the detection of this signal difficult. In order to improve the accuracy and confidence of detection, a re-identification framework in which a tsunamigenic signal is detected via the scan of a network of hydrodynamic stations with water level sensing is performed. The aim is to attempt the re-identification of the same signatures as the tsunami wave spatially propagates through the hydrodynamic stations sensing network. The re-identification of the tsunamigenic signal is technically possible since the tsunami signal at the open ocean itself conserves its birthmarks relating it to the source event. As well as supporting the initial detection and improving the confidence of detection, a re-identified signal is indicative of the spatial range of the signal, and thereby it can be used to facilitate the identification of certain background signals such as wind waves which do not have as large a spatial reach as tsunamis. In this paper, the proposed methodology for the automatic detection of tsunamigenic signals has been achieved using open data from NOAA with a recorded tsunami event in the Pacific Ocean. The new approach will be tested in the future on other oceanic regions including the Mediteranean Sea and North East Atlantic Ocean zones. Both authors acknowledge that the current research is currently conducted under the TRIDEC IP FP7 project[1] which involves the development of a system of systems for collaborative, complex and critical decision-support in evolving crises. [1] TRIDEC IP ICT-2009.4.3 Intelligent Information Management Project Reference: 258723. http://www.tridec-online.eu/home

Evaluating the performance of the newly-launched Landsat 8 sensor in detecting and mapping the spatial configuration of water hyacinth (Eichhornia crassipes) in inland lakes, Zimbabwe

NASA Astrophysics Data System (ADS)

Dube, Timothy; Mutanga, Onisimo; Sibanda, Mbulisi; Bangamwabo, Victor; Shoko, Cletah

2017-08-01

The remote sensing of freshwater resources is increasingly becoming important, due to increased patterns of water use and the current or projected impacts of climate change and the rapid invasion by lethal water weeds. This study therefore sought to explore the potential of the recently-launched Landsat 8 OLI/TIRS sensor in mapping invasive species in inland lakes. Specifically, the study compares the performance of the newly-launched Landsat 8 sensor, with more advanced sensor design and image acquisition approach to the traditional Landsat-7 ETM+ in detecting and mapping the water hyacinth (Eichhornia crassipes) invasive species across Lake Chivero, in Zimbabwe. The analysis of variance test was used to identify windows of spectral separability between water hyacinth and other land cover types. The results showed that portions of the visible (B3), NIR (B4), as well as the shortwave bands (Band 8, 9 and 10) of both Landsat 8 OLI and Landsat 7 ETM, exhibited windows of separability between water hyacinth and other land cover types. It was also observed that on the use of Landsat 8 OLI produced high overall classification accuracy of 72%, when compared Landsat 7 ETM, which yielded lower accuracy of 57%. Water hyacinth had optimal accuracies (i.e. 92%), when compared to other land cover types, based on Landsat 8 OLI data. However, when using Landsat 7 ETM data, classification accuracies of water hyacinth were relatively lower (i.e. 67%), when compared to other land cover types (i.e. water with accuracy of 100%). Spectral curves of the old, intermediate and the young water hyacinth in Lake Chivero based on: (a) Landsat 8 OLI, and (b) Landsat 7 ETM were derived. Overall, the findings of this study underscores the relevance of the new generation multispectral sensors in providing primary data-source required for mapping the spatial distribution, and even configuration of water weeds at lower or no cost over time and space.

Data Processing and Quality Evaluation of a Boat-Based Mobile Laser Scanning System

PubMed Central

Vaaja, Matti; Kukko, Antero; Kaartinen, Harri; Kurkela, Matti; Kasvi, Elina; Flener, Claude; Hyyppä, Hannu; Hyyppä, Juha; Järvelä, Juha; Alho, Petteri

2013-01-01

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0–1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data. PMID:24048340

Data processing and quality evaluation of a boat-based mobile laser scanning system.

PubMed

Vaaja, Matti; Kukko, Antero; Kaartinen, Harri; Kurkela, Matti; Kasvi, Elina; Flener, Claude; Hyyppä, Hannu; Hyyppä, Juha; Järvelä, Juha; Alho, Petteri

2013-09-17

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0-1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data.

Microfluidic CODES: a scalable multiplexed electronic sensor for orthogonal detection of particles in microfluidic channels.

PubMed

Liu, Ruxiu; Wang, Ningquan; Kamili, Farhan; Sarioglu, A Fatih

2016-04-21

Numerous biophysical and biochemical assays rely on spatial manipulation of particles/cells as they are processed on lab-on-a-chip devices. Analysis of spatially distributed particles on these devices typically requires microscopy negating the cost and size advantages of microfluidic assays. In this paper, we introduce a scalable electronic sensor technology, called microfluidic CODES, that utilizes resistive pulse sensing to orthogonally detect particles in multiple microfluidic channels from a single electrical output. Combining the techniques from telecommunications and microfluidics, we route three coplanar electrodes on a glass substrate to create multiple Coulter counters producing distinct orthogonal digital codes when they detect particles. We specifically design a digital code set using the mathematical principles of Code Division Multiple Access (CDMA) telecommunication networks and can decode signals from different microfluidic channels with >90% accuracy through computation even if these signals overlap. As a proof of principle, we use this technology to detect human ovarian cancer cells in four different microfluidic channels fabricated using soft lithography. Microfluidic CODES offers a simple, all-electronic interface that is well suited to create integrated, low-cost lab-on-a-chip devices for cell- or particle-based assays in resource-limited settings.

An information-theoretic approach to designing the plane spacing for multifocal plane microscopy

PubMed Central

Tahmasbi, Amir; Ram, Sripad; Chao, Jerry; Abraham, Anish V.; Ward, E. Sally; Ober, Raimund J.

2015-01-01

Multifocal plane microscopy (MUM) is a 3D imaging modality which enables the localization and tracking of single molecules at high spatial and temporal resolution by simultaneously imaging distinct focal planes within the sample. MUM overcomes the depth discrimination problem of conventional microscopy and allows high accuracy localization of a single molecule in 3D along the z-axis. An important question in the design of MUM experiments concerns the appropriate number of focal planes and their spacings to achieve the best possible 3D localization accuracy along the z-axis. Ideally, it is desired to obtain a 3D localization accuracy that is uniform over a large depth and has small numerical values, which guarantee that the single molecule is continuously detectable. Here, we address this concern by developing a plane spacing design strategy based on the Fisher information. In particular, we analyze the Fisher information matrix for the 3D localization problem along the z-axis and propose spacing scenarios termed the strong coupling and the weak coupling spacings, which provide appropriate 3D localization accuracies. Using these spacing scenarios, we investigate the detectability of the single molecule along the z-axis and study the effect of changing the number of focal planes on the 3D localization accuracy. We further review a software module we recently introduced, the MUMDesignTool, that helps to design the plane spacings for a MUM setup. PMID:26113764

Identification of mosquito larval habitats in high resolution satellite data

NASA Astrophysics Data System (ADS)

Kiang, Richard K.; Hulina, Stephanie M.; Masuoka, Penny M.; Claborn, David M.

2003-09-01

Mosquito-born infectious diseases are a serious public health concern, not only for the less developed countries, but also for developed countries like the U.S. Larviciding is an effective method for vector control and adverse effects to non-target species are minimized when mosquito larval habitats are properly surveyed and treated. Remote sensing has proven to be a useful technique for large-area ground cover mapping, and hence, is an ideal tool for identifying potential larval habitats. Locating small larval habitats, however, requires data with very high spatial resolution. Textural and contextual characteristics become increasingly evident at higher spatial resolution. Per-pixel classification often leads to suboptimal results. In this study, we use pan-sharpened Ikonos data, with a spatial resolution approaching 1 meter, to classify potential mosquito larval habitats for a test site in South Korea. The test site is in a predominantly agricultural region. When spatial characteristics were used in conjunction with spectral data, reasonably good classification accuracy was obtained for the test site. In particular, irrigation and drainage ditches are important larval habitats but their footprints are too small to be detected with the original spectral data at 4-meter resolution. We show that the ditches are detectable using automated classification on pan-sharpened data.

Single photon counting fluorescence lifetime detection of pericellular oxygen concentrations

NASA Astrophysics Data System (ADS)

Hosny, Neveen A.; Lee, David A.; Knight, Martin M.

2012-01-01

Fluorescence lifetime imaging microscopy offers a non-invasive method for quantifying local oxygen concentrations. However, existing methods are either invasive, require custom-made systems, or show limited spatial resolution. Therefore, these methods are unsuitable for investigation of pericellular oxygen concentrations. This study describes an adaptation of commercially available equipment which has been optimized for quantitative extracellular oxygen detection with high lifetime accuracy and spatial resolution while avoiding systematic photon pile-up. The oxygen sensitive fluorescent dye, tris(2,2'-bipyridyl)ruthenium(II) chloride hexahydrate [Ru(bipy)3]2+, was excited using a two-photon excitation laser. Lifetime was measured using a Becker & Hickl time-correlated single photon counting, which will be referred to as a TCSPC card. [Ru(bipy)3]2+ characterization studies quantified the influences of temperature, pH, cellular culture media and oxygen on the fluorescence lifetime measurements. This provided a precisely calibrated and accurate system for quantification of pericellular oxygen concentration based on measured lifetimes. Using this technique, quantification of oxygen concentrations around isolated viable chondrocytes, seeded in three-dimensional agarose gel, revealed a subpopulation of cells that exhibited significant spatial oxygen gradients such that oxygen concentration reduced with increasing proximity to the cell. This technique provides a powerful tool for quantifying spatial oxygen gradients within three-dimensional cellular models.

Single photon counting fluorescence lifetime detection of pericellular oxygen concentrations.

PubMed

Hosny, Neveen A; Lee, David A; Knight, Martin M

2012-01-01

Fluorescence lifetime imaging microscopy offers a non-invasive method for quantifying local oxygen concentrations. However, existing methods are either invasive, require custom-made systems, or show limited spatial resolution. Therefore, these methods are unsuitable for investigation of pericellular oxygen concentrations. This study describes an adaptation of commercially available equipment which has been optimized for quantitative extracellular oxygen detection with high lifetime accuracy and spatial resolution while avoiding systematic photon pile-up. The oxygen sensitive fluorescent dye, tris(2,2'-bipyridyl)ruthenium(II) chloride hexahydrate [Ru(bipy)(3)](2+), was excited using a two-photon excitation laser. Lifetime was measured using a Becker & Hickl time-correlated single photon counting, which will be referred to as a TCSPC card. [Ru(bipy)(3)](2+) characterization studies quantified the influences of temperature, pH, cellular culture media and oxygen on the fluorescence lifetime measurements. This provided a precisely calibrated and accurate system for quantification of pericellular oxygen concentration based on measured lifetimes. Using this technique, quantification of oxygen concentrations around isolated viable chondrocytes, seeded in three-dimensional agarose gel, revealed a subpopulation of cells that exhibited significant spatial oxygen gradients such that oxygen concentration reduced with increasing proximity to the cell. This technique provides a powerful tool for quantifying spatial oxygen gradients within three-dimensional cellular models.

Rapid Prototyping of Hyperspectral Image Analysis Algorithms for Improved Invasive Species Decision Support Tools

NASA Astrophysics Data System (ADS)

Bruce, L. M.; Ball, J. E.; Evangilista, P.; Stohlgren, T. J.

2006-12-01

Nonnative invasive species adversely impact ecosystems, causing loss of native plant diversity, species extinction, and impairment of wildlife habitats. As a result, over the past decade federal and state agencies and nongovernmental organizations have begun to work more closely together to address the management of invasive species. In 2005, approximately 500M dollars was budgeted by U.S. Federal Agencies for the management of invasive species. Despite extensive expenditures, most of the methods used to detect and quantify the distribution of these invaders are ad hoc, at best. Likewise, decisions on the type of management techniques to be used or evaluation of the success of these methods are typically non-systematic. More efficient methods to detect or predict the occurrence of these species, as well as the incorporation of this knowledge into decision support systems, are greatly needed. In this project, rapid prototyping capabilities (RPC) are utilized for an invasive species application. More precisely, our recently developed analysis techniques for hyperspectral imagery are being prototyped for inclusion in the national Invasive Species Forecasting System (ISFS). The current ecological forecasting tools in ISFS will be compared to our hyperspectral-based invasives prediction algorithms to determine if/how the newer algorithms enhance the performance of ISFS. The PIs have researched the use of remotely sensed multispectral and hyperspectral reflectance data for the detection of invasive vegetative species. As a result, the PI has designed, implemented, and benchmarked various target detection systems that utilize remotely sensed data. These systems have been designed to make decisions based on a variety of remotely sensed data, including high spectral/spatial resolution hyperspectral signatures (1000's of spectral bands, such as those measured using ASD handheld devices), moderate spectral/spatial resolution hyperspectral images (100's of spectral bands, such as Hyperion imagery), and low spectral/spatial resolution images (such as MODIS imagery). These algorithms include hyperspectral exploitation methods such as stepwise-LDA band selection, optimized spectral band grouping, and stepwise PCA component selection. The PIs have extensive experience with combining these recently- developed methods with conventional classifiers to form an end-to-end automated target recognition (ATR) system for detecting invasive species. The outputs of these systems can be invasive prediction maps, as well as quantitative accuracy assessments like confusion matrices, user accuracies, and producer accuracies. For all of these research endeavors, the PIs have developed numerous advanced signal and image processing methodologies, as well a suite of associated software modules. However, the use of the prototype software modules has been primarily contained to Mississippi State University. The project described in this presentation and paper will enable future systematic inclusion of these software modules into a DSS with national scope.

Spatial accuracy assessment in natural resources and environmental sciences: Second International Symposium

Treesearch

H. Todd Mowrer; Raymond L. Czaplewski; R. H. Hamre

1996-01-01

This international symposium on theory and techniques for assessing the accuracy of spatial data and spatial analyses included more than ninety presentations by representatives from government, academic, and private institutions in over twenty countries throughout the world. To encourage interactions across disciplines, presentations in the general subject areas of...

Inter-Comparison of Lightning Trends from Ground-Based Networks During Severe Weather: Applications Toward GLM

NASA Technical Reports Server (NTRS)

Carey, Lawrence D.; Schultz, Chris J.; Petersen, Walter A.; Rudlosky, Scott D.; Bateman, Monte; Cecil, Daniel J.; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

The planned GOES-R Geostationary Lightning Mapper (GLM) will provide total lightning data on the location and intensity of thunderstorms over a hemispheric spatial domain. Ongoing GOES-R research activities are demonstrating the utility of total flash rate trends for enhancing forecasting skill of severe storms. To date, GLM total lightning proxy trends have been well served by ground-based VHF systems such as the Northern Alabama Lightning Mapping Array (NALMA). The NALMA (and other similar networks in Washington DC and Oklahoma) provide high detection efficiency (> 90%) and location accuracy (< 1 km) observations of total lightning within about 150 km from network center. To expand GLM proxy applications for high impact convective weather (e.g., severe, aviation hazards), it is desirable to investigate the utility of additional sources of continuous lightning that can serve as suitable GLM proxy over large spatial scales (order 100 s to 1000 km or more), including typically data denied regions such as the oceans. Potential sources of GLM proxy include ground-based long-range (regional or global) VLF/LF lightning networks such as the relatively new Vaisala Global Lightning Dataset (GLD360) and Weatherbug Total Lightning Network (WTLN). Before using these data in GLM research applications, it is necessary to compare them with LMAs and well-quantified cloud-to-ground (CG) lightning networks, such as Vaisala s National Lightning Detection Network (NLDN), for assessment of total and CG lightning location accuracy, detection efficiency and flash rate trends. Preliminary inter-comparisons from these lightning networks during selected severe weather events will be presented and their implications discussed.

Enhanced heat discrimination in congenital blindness.

PubMed

Slimani, Hocine; Ptito, Maurice; Kupers, Ron

2015-04-15

There is substantial evidence that congenitally blind individuals perform better than normally sighted controls in a variety of auditory, tactile and olfactory discrimination tasks. However, little is known about the capacity of blind individuals to make fine discriminatory judgments in the thermal domain. We therefore compared the capacity to detect small temperature increases in innocuous heat in a group of 12 congenitally blind and 12 age and sex-matched normally sighted participants. In addition, we also tested for group differences in the effects of spatial summation on temperature discrimination. Thermal stimuli were delivered with either a 2.56 or 9 cm(2) Peltier-based thermode. We applied for 5-8s lasting non-painful thermal stimuli to the forearm and asked participants to detect small increments in temperature (ΔT = 0.4, 0.8, 1.2 or 1.6°C) that occurred at random time intervals. Blank trials (ΔT = 0°C) were also included to test for false positive responses. We used signal detection theory model to analyze the data. Our data revealed that blind participants have a higher accuracy than the sighted (d': Blind=2.4 ± 1.0, Sighted=1.8 ± 0.7, p=0.025), regardless of the size of the stimulated skin surface or magnitude of the temperature shift. Increasing the size of the stimulated skin area increased the response criterion in the blind (p=0.022) but not in the sighted. Together, these findings show that congenitally blind individuals have enhanced temperature discrimination accuracy and are more susceptible to spatial summation of heat. Copyright © 2015 Elsevier B.V. All rights reserved.

The Direct Georeferencing Application and Performance Analysis of Uav Helicopter in Gcp-Free Area

NASA Astrophysics Data System (ADS)

Lo, C. F.; Tsai, M. L.; Chiang, K. W.; Chu, C. H.; Tsai, G. J.; Cheng, C. K.; El-Sheimy, N.; Ayman, H.

2015-08-01

There are many disasters happened because the weather changes extremely in these years. To facilitate applications such as environment detection or monitoring becomes very important. Therefore, the development of rapid low cost systems for collecting near real-time spatial information is very critical. Rapid spatial information collection has become an emerging trend for remote sensing and mapping applications. This study develops a Direct Georeferencing (DG) based Unmanned Aerial Vehicle (UAV) helicopter photogrammetric platform where an Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) integrated Positioning and Orientation System (POS) system is implemented to provide the DG capability of the platform. The performance verification indicates that the proposed platform can capture aerial images successfully. A flight test is performed to verify the positioning accuracy in DG mode without using Ground Control Points (GCP). The preliminary results illustrate that horizontal DG positioning accuracies in the x and y axes are around 5 meter with 100 meter flight height. The positioning accuracy in the z axis is less than 10 meter. Such accuracy is good for near real-time disaster relief. The DG ready function of proposed platform guarantees mapping and positioning capability even in GCP free environments, which is very important for rapid urgent response for disaster relief. Generally speaking, the data processing time for the DG module, including POS solution generalization, interpolation, Exterior Orientation Parameters (EOP) generation, and feature point measurements, is less than 1 hour.

Long fiber Bragg grating sensor interrogation using discrete-time microwave photonic filtering techniques.

PubMed

Ricchiuti, Amelia Lavinia; Barrera, David; Sales, Salvador; Thevenaz, Luc; Capmany, José

2013-11-18

A novel technique for interrogating photonic sensors based on long fiber Bragg gratings (FBGs) is presented and experimentally demonstrated, dedicated to detect the presence and the precise location of several spot events. The principle of operation is based on a technique used to analyze microwave photonics (MWP) filters. The long FBGs are used as quasi-distributed sensors. Several hot-spots can be detected along the FBG with a spatial accuracy under 0.5 mm using a modulator and a photo-detector (PD) with a modest bandwidth of less than 1 GHz. The proposed interrogation system is intrinsically robust against environmental changes.

Remote Sensing Data Fusion to Detect Illicit Crops and Unauthorized Airstrips

NASA Astrophysics Data System (ADS)

Pena, J. A.; Yumin, T.; Liu, H.; Zhao, B.; Garcia, J. A.; Pinto, J.

2018-04-01

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.

Endogenous synchronous fluorescence spectroscopy (SFS) of basal cell carcinoma-initial study

NASA Astrophysics Data System (ADS)

Borisova, E.; Zhelyazkova, Al.; Keremedchiev, M.; Penkov, N.; Semyachkina-Glushkovskaya, O.; Avramov, L.

2016-01-01

The human skin is a complex, multilayered and inhomogeneous organ with spatially varying optical properties. Analysis of cutaneous fluorescence spectra could be a very complicated task; therefore researchers apply complex mathematical tools for data evaluation, or try to find some specific approaches, that would simplify the spectral analysis. Synchronous fluorescence spectroscopy (SFS) allows improving the spectral resolution, which could be useful for the biological tissue fluorescence characterization and could increase the tumour detection diagnostic accuracy.

Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information

NASA Astrophysics Data System (ADS)

Avbelj, Janja; Iwaszczuk, Dorota; Müller, Rupert; Reinartz, Peter; Stilla, Uwe

2015-02-01

For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches.

Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

USGS Publications Warehouse

Garrity, Steven R.; Allen, Craig D.; Brumby, Steven P.; Gangodagamage, Chandana; McDowell, Nate G.; Cai, D. Michael

2013-01-01

Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper (Pinus edulis-Juniperus monosperma) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas experiencing wildfire and management activity. -Our results demonstrate that unsupervised clustering of bi-temporal NDVI and RGI differences can be used to detect tree mortality resulting from numerous causes and in several forest cover types.

Frequency correction method for improved spatial correlation of hyperpolarized 13C metabolites and anatomy.

PubMed

Cunningham, Charles H; Dominguez Viqueira, William; Hurd, Ralph E; Chen, Albert P

2014-02-01

Blip-reversed echo-planar imaging (EPI) is investigated as a method for measuring and correcting the spatial shifts that occur due to bulk frequency offsets in (13)C metabolic imaging in vivo. By reversing the k-space trajectory for every other time point, the direction of the spatial shift for a given frequency is reversed. Here, mutual information is used to find the 'best' alignment between images and thereby measure the frequency offset. Time-resolved 3D images of pyruvate/lactate/urea were acquired with 5 s temporal resolution over a 1 min duration in rats (N = 6). For each rat, a second injection was performed with the demodulation frequency purposely mis-set by +35 Hz, to test the correction for erroneous shifts in the images. Overall, the shift induced by the 35 Hz frequency offset was 5.9 ± 0.6 mm (mean ± standard deviation). This agrees well with the expected 5.7 mm shift based on the 2.02 ms delay between k-space lines (giving 30.9 Hz per pixel). The 0.6 mm standard deviation in the correction corresponds to a frequency-detection accuracy of 4 Hz. A method was presented for ensuring the spatial registration between (13)C metabolic images and conventional anatomical images when long echo-planar readouts are used. The frequency correction method was shown to have an accuracy of 4 Hz. Summing the spatially corrected frames gave a signal-to-noise ratio (SNR) improvement factor of 2 or greater, compared with the highest single frame. Copyright © 2013 John Wiley & Sons, Ltd.

Mapping Chinese tallow with color-infrared photography

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, G.A.; Sapkota, S.K.; Seeger, E.B.; Martella, K.D.

2002-01-01

Airborne color-infrared photography (CIR) (1:12,000 scale) was used to map localized occurrences of the widespread and aggressive Chinese tallow (Sapium sebiferum), an invasive species. Photography was collected during senescence when Chinese tallow's bright red leaves presented a high spectral contrast within the native bottomland hardwood and upland forests and marsh land-cover types. Mapped occurrences were conservative because not all senescing tallow leaves are bright red simultaneously. To simulate low spectral but high spatial resolution satellite/airborne image and digital video data, the CIR photography was transformed into raster images at spatial resolutions approximating 0.5 in and 1.0 m. The image data were then spectrally classified for the occurrence of bright red leaves associated with senescing Chinese tallow. Classification accuracies were greater than 95 percent at both spatial resolutions. There was no significant difference in either forest in the detection of tallow or inclusion of non-tallow trees associated with the two spatial resolutions. In marshes, slightly more tallow occurrences were mapped with the lower spatial resolution, but there were also more misclassifications of native land covers as tallow. Combining all land covers, there was no difference at detecting tallow occurrences (equal omission errors) between the two resolutions, but the higher spatial resolution was associated with less inclusion of non-tallow land covers as tallow (lower commission error). Overall, these results confirm that high spatial (???1 m) but low spectral resolution remote sensing data can be used for mapping Chinese tallow trees in dominant environments found in coastal and adjacent upland landscapes.

Detecting and removing multiplicative spatial bias in high-throughput screening technologies.

PubMed

Caraus, Iurie; Mazoure, Bogdan; Nadon, Robert; Makarenkov, Vladimir

2017-10-15

Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias. We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative. The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens. The AssayCorrector program, implemented in R, is available on CRAN. makarenkov.vladimir@uqam.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Wide Swath Stereo Mapping from Gaofen-1 Wide-Field-View (WFV) Images Using Calibration

PubMed Central

Chen, Shoubin; Liu, Jingbin; Huang, Wenchao

2018-01-01

The development of Earth observation systems has changed the nature of survey and mapping products, as well as the methods for updating maps. Among optical satellite mapping methods, the multiline array stereo and agile stereo modes are the most common methods for acquiring stereo images. However, differences in temporal resolution and spatial coverage limit their application. In terms of this issue, our study takes advantage of the wide spatial coverage and high revisit frequencies of wide swath images and aims at verifying the feasibility of stereo mapping with the wide swath stereo mode and reaching a reliable stereo accuracy level using calibration. In contrast with classic stereo modes, the wide swath stereo mode is characterized by both a wide spatial coverage and high-temporal resolution and is capable of obtaining a wide range of stereo images over a short period. In this study, Gaofen-1 (GF-1) wide-field-view (WFV) images, with total imaging widths of 800 km, multispectral resolutions of 16 m and revisit periods of four days, are used for wide swath stereo mapping. To acquire a high-accuracy digital surface model (DSM), the nonlinear system distortion in the GF-1 WFV images is detected and compensated for in advance. The elevation accuracy of the wide swath stereo mode of the GF-1 WFV images can be improved from 103 m to 30 m for a DSM with proper calibration, meeting the demands for 1:250,000 scale mapping and rapid topographic map updates and showing improved efficacy for satellite imaging. PMID:29494540

Improved segmentation of cerebellar structures in children

PubMed Central

Narayanan, Priya Lakshmi; Boonazier, Natalie; Warton, Christopher; Molteno, Christopher D; Joseph, Jesuchristopher; Jacobson, Joseph L; Jacobson, Sandra W; Zöllei, Lilla; Meintjes, Ernesta M

2016-01-01

Background Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available. New method The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9–13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to ‘gold standard’ manual tracings. Results Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55–0.91). Comparison with existing methods In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (2014) in adults (across all lobules mean DSC = 0.73, range 0.40–0.89). Conclusions CAPCA18 and the associated multi atlases of the training subjects yield improved segmentation of cerebellar structures in children. PMID:26743973

Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos

NASA Astrophysics Data System (ADS)

Dorigo, Wouter; Lucieer, Arko; Podobnikar, Tomaž; Čarni, Andraž

2012-10-01

Japanese knotweed (Fallopia japonica) is listed among 100 of the World's worst invasive alien species and poses an increasing threat to ecosystems and agriculture in Northern America, Europe, and Oceania. This study proposes a remote sensing method to detect local occurrences of F. japonica from low-cost digital orthophotos taken in early spring and summer by concurrently exploring its temporal, spectral, and spatial characteristics. Temporal characteristics of the species are quantified by a band ratio calculated from the green and red spectral channels of both images. The normalized difference vegetation index was used to capture the high near-infrared (NIR) reflectance of F. japonica in summer while the characteristic texture of F. japonica is quantified by calculating gray level co-occurrence matrix (GLCM) measures. After establishing the optimum kernel size to quantify texture, the different input features (spectral, spatial, and texture) were stacked and used as input to the random forest (RF) classifier. The proposed method was tested for a built-up and semi-natural area in Slovenia. The spectral, spatial, and temporal provided an equally important contribution for differentiating F. japonica from other land cover types. The combination of all signatures resulted in a producer accuracy of 90.3% and a user accuracy of 98.1% for F. japonica when validation was based on independent regions of interest. A producer accuracy of 61.4% was obtained for F. japonica when comparing the classification result with all occurrences of F. japonica identified during a field validation campaign. This is an encouraging result given the very small patches in which the species usually occur and the high degree of intermingling with other plants. All hot spots were identified and even likely infestations of F. japonica that had remained undiscovered during the field campaign were detected. The probability images resulting from the RF classifier can be used to reduce the relatively large number of false alarms and may assist in targeted eradication measures. Classification skill only slightly reduced when NIR information was not considered, which is an important recognition with regard to transferability of the method to the most basic type of digital color orthophotos. The possibility to use orthophotos, which at most municipalities are commonly available and easily accessible, facilitates an immediate implementation of the approach in situations where intervention is urgent.

Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom

PubMed Central

Català, Andreu; Rodríguez Martín, Daniel; van der Aa, Nico; Chen, Wei; Rauterberg, Matthias

2013-01-01

Background Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. Objective The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. Methods We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. Results We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. Conclusions The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position. PMID:25098265

Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom.

PubMed

Takač, Boris; Català, Andreu; Rodríguez Martín, Daniel; van der Aa, Nico; Chen, Wei; Rauterberg, Matthias

2013-07-15

Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position.

Detecting declines in the abundance of a bull trout (Salvelinus confluentus) population: Understanding the accuracy, precision, and costs of our efforts

USGS Publications Warehouse

Al-Chokhachy, R.; Budy, P.; Conner, M.

2009-01-01

Using empirical field data for bull trout (Salvelinus confluentus), we evaluated the trade-off between power and sampling effort-cost using Monte Carlo simulations of commonly collected mark-recapture-resight and count data, and we estimated the power to detect changes in abundance across different time intervals. We also evaluated the effects of monitoring different components of a population and stratification methods on the precision of each method. Our results illustrate substantial variability in the relative precision, cost, and information gained from each approach. While grouping estimates by age or stage class substantially increased the precision of estimates, spatial stratification of sampling units resulted in limited increases in precision. Although mark-resight methods allowed for estimates of abundance versus indices of abundance, our results suggest snorkel surveys may be a more affordable monitoring approach across large spatial scales. Detecting a 25% decline in abundance after 5 years was not possible, regardless of technique (power = 0.80), without high sampling effort (48% of study site). Detecting a 25% decline was possible after 15 years, but still required high sampling efforts. Our results suggest detecting moderate changes in abundance of freshwater salmonids requires considerable resource and temporal commitments and highlight the difficulties of using abundance measures for monitoring bull trout populations.

The influence on the interferometry due to the instability of ground-based synthetic aperture radar work platform

NASA Astrophysics Data System (ADS)

Tao, Gang; Wei, Guohua; Wang, Xu; Kong, Ming

2018-03-01

There has been increased interest over several decades for applying ground-based synthetic aperture radar (GB-SAR) for monitoring terrain displacement. GB-SAR can achieve multitemporal surface deformation maps of the entire terrain with high spatial resolution and submilimetric accuracy due to the ability of continuous monitoring a certain area day and night regardless of the weather condition. The accuracy of the interferometric measurement result is very important. In this paper, the basic principle of InSAR is expounded, the influence of the platform's instability on the interferometric measurement results are analyzed. The error sources of deformation detection estimation are analyzed using precise geometry of imaging model. Finally, simulation results demonstrates the validity of our analysis.

Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern.

PubMed

Fujisawa, Junya; Touyama, Hideaki; Hirose, Michitaka

2008-01-01

In this paper, alpha band modulation during visual spatial attention without visual stimuli was focused. Visual spatial attention has been expected to provide a new channel of non-invasive independent brain computer interface (BCI), but little work has been done on the new interfacing method. The flickering stimuli used in previous work cause a decline of independency and have difficulties in a practical use. Therefore we investigated whether visual spatial attention could be detected without such stimuli. Further, the common spatial patterns (CSP) were for the first time applied to the brain states during visual spatial attention. The performance evaluation was based on three brain states of left, right and center direction attention. The 30-channel scalp electroencephalographic (EEG) signals over occipital cortex were recorded for five subjects. Without CSP, the analyses made 66.44 (range 55.42 to 72.27) % of average classification performance in discriminating left and right attention classes. With CSP, the averaged classification accuracy was 75.39 (range 63.75 to 86.13) %. It is suggested that CSP is useful in the context of visual spatial attention, and the alpha band modulation during visual spatial attention without flickering stimuli has the possibility of a new channel for independent BCI as well as motor imagery.

Accuracy of Scale Conceptions in Science: Mental Maneuverings across Many Orders of Spatial Magnitude

ERIC Educational Resources Information Center

Tretter, Thomas R.; Jones, M. Gail; Minogue, James

2006-01-01

The use of unifying themes that span the various branches of science is recommended to enhance curricular coherence in science instruction. Conceptions of spatial scale are one such unifying theme. This research explored the accuracy of spatial scale conceptions of science phenomena across a spectrum of 215 participants: fifth grade, seventh…

Are You Sure the Library Is That Way? Metacognitive Monitoring of Spatial Judgments

ERIC Educational Resources Information Center

Stevens, Christopher A.; Carlson, Richard A.

2016-01-01

Many studies have examined how people recall the locations of objects in spatial layouts. However, little is known about how people monitor the accuracy of judgments based on those memories. The goal of the present experiments was to examine the effect of reference frame characteristics on metacognitive accuracy for spatial judgments. Reference…

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronald; Russell, Jeffrey A.; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

Spatial and thematic assessment of object-based forest stand delineation using an OFA-matrix

NASA Astrophysics Data System (ADS)

Hernando, A.; Tiede, D.; Albrecht, F.; Lang, S.

2012-10-01

The delineation and classification of forest stands is a crucial aspect of forest management. Object-based image analysis (OBIA) can be used to produce detailed maps of forest stands from either orthophotos or very high resolution satellite imagery. However, measures are then required for evaluating and quantifying both the spatial and thematic accuracy of the OBIA output. In this paper we present an approach for delineating forest stands and a new Object Fate Analysis (OFA) matrix for accuracy assessment. A two-level object-based orthophoto analysis was first carried out to delineate stands on the Dehesa Boyal public land in central Spain (Avila Province). Two structural features were first created for use in class modelling, enabling good differentiation between stands: a relational tree cover cluster feature, and an arithmetic ratio shadow/tree feature. We then extended the OFA comparison approach with an OFA-matrix to enable concurrent validation of thematic and spatial accuracies. Its diagonal shows the proportion of spatial and thematic coincidence between a reference data and the corresponding classification. New parameters for Spatial Thematic Loyalty (STL), Spatial Thematic Loyalty Overall (STLOVERALL) and Maximal Interfering Object (MIO) are introduced to summarise the OFA-matrix accuracy assessment. A stands map generated by OBIA (classification data) was compared with a map of the same area produced from photo interpretation and field data (reference data). In our example the OFA-matrix results indicate good spatial and thematic accuracies (>65%) for all stand classes except for the shrub stands (31.8%), and a good STLOVERALL (69.8%). The OFA-matrix has therefore been shown to be a valid tool for OBIA accuracy assessment.

Study on multispectral imaging detection and recognition

NASA Astrophysics Data System (ADS)

Jun, Wang; Na, Ding; Gao, Jiaobo; Yu, Hu; Jun, Wu; Li, Junna; Zheng, Yawei; Fei, Gao; Sun, Kefeng

2009-07-01

Multispectral imaging detecting technology use target radiation character in spectral spatial distribution and relation between spectral and image to detect target and remote sensing measure. Its speciality is multi channel, narrow bandwidth, large amount of information, high accuracy. The ability of detecting target in environment of clutter, camouflage, concealment and beguilement is improved. At present, spectral imaging technology in the range of multispectral and hyperspectral develop greatly. The multispectral imaging equipment of unmanned aerial vehicle can be used in mine detection, information, surveillance and reconnaissance. Spectral imaging spectrometer operating in MWIR and LWIR has already been applied in the field of remote sensing and military in the advanced country. The paper presents the technology of multispectral imaging. It can enhance the reflectance, scatter and radiation character of the artificial targets among nature background. The targets among complex background and camouflage/stealth targets can be effectively identified. The experiment results and the data of spectral imaging is obtained.

Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities

NASA Astrophysics Data System (ADS)

Ossés de Eicker, Margarita; Zah, Rainer; Triviño, Rubén; Hurni, Hans

The spatial accuracy of top-down traffic emission inventory maps obtained with a simplified disaggregation method based on street density was assessed in seven mid-sized Chilean cities. Each top-down emission inventory map was compared against a reference, namely a more accurate bottom-up emission inventory map from the same study area. The comparison was carried out using a combination of numerical indicators and visual interpretation. Statistically significant differences were found between the seven cities with regard to the spatial accuracy of their top-down emission inventory maps. In compact cities with a simple street network and a single center, a good accuracy of the spatial distribution of emissions was achieved with correlation values>0.8 with respect to the bottom-up emission inventory of reference. In contrast, the simplified disaggregation method is not suitable for complex cities consisting of interconnected nuclei, resulting in correlation values<0.5. Although top-down disaggregation of traffic emissions generally exhibits low accuracy, the accuracy is significantly higher in compact cities and might be further improved by applying a correction factor for the city center. Therefore, the method can be used by local environmental authorities in cities with limited resources and with little knowledge on the pollution situation to get an overview on the spatial distribution of the emissions generated by traffic activities.

An implicit spatial and high-order temporal finite difference scheme for 2D acoustic modelling

NASA Astrophysics Data System (ADS)

Wang, Enjiang; Liu, Yang

2018-01-01

The finite difference (FD) method exhibits great superiority over other numerical methods due to its easy implementation and small computational requirement. We propose an effective FD method, characterised by implicit spatial and high-order temporal schemes, to reduce both the temporal and spatial dispersions simultaneously. For the temporal derivative, apart from the conventional second-order FD approximation, a special rhombus FD scheme is included to reach high-order accuracy in time. Compared with the Lax-Wendroff FD scheme, this scheme can achieve nearly the same temporal accuracy but requires less floating-point operation times and thus less computational cost when the same operator length is adopted. For the spatial derivatives, we adopt the implicit FD scheme to improve the spatial accuracy. Apart from the existing Taylor series expansion-based FD coefficients, we derive the least square optimisation based implicit spatial FD coefficients. Dispersion analysis and modelling examples demonstrate that, our proposed method can effectively decrease both the temporal and spatial dispersions, thus can provide more accurate wavefields.

First detection of precursory ground inflation of a small phreatic eruption by InSAR

NASA Astrophysics Data System (ADS)

Kobayashi, Tomokazu; Morishita, Yu; Munekane, Hiroshi

2018-06-01

Phreatic eruptions are caused by pressurization of geothermal fluid sources at shallow levels. They are relatively small compared to typical magmatic eruptions, but can be very hazardous. However, owing to their small magnitudes, their occurrences are difficult to predict. Here we show the detection of locally distributed ground inflation preceding a small phreatic eruption at the Hakone volcano, Japan, through the application of interferometric synthetic aperture radar analysis. The ground inflation proceeded the eruption at slow speed of ∼5 mm/month with a spatial size of ∼200 m in the early stage, and then it accelerated 2 months before the eruption that occurred for the first time in 800-900 yrs. The ground uplift reached ∼30 cm, and the eruption occurred nearby the most deformed part. The deformation speed correlated well with inflation of spherical source located at 4.8 km below sea level, thus suggesting that heat and/or volcanic fluid supply from the spherical source, maybe magma reservoir, directly drove the subsurface hydrothermal activity. Our results demonstrate that high-spatial-resolution deformation data can be a good indicator of subsurface pressure conditions with pinpoint spatial accuracy during the preparatory process of phreatic eruptions.

Investigating the spatial accuracy of CBCT-guided cranial radiosurgery: A phantom end-to-end test study.

PubMed

Calvo-Ortega, Juan-Francisco; Hermida-López, Marcelino; Moragues-Femenía, Sandra; Pozo-Massó, Miquel; Casals-Farran, Joan

2017-03-01

To evaluate the spatial accuracy of a frameless cone-beam computed tomography (CBCT)-guided cranial radiosurgery (SRS) using an end-to-end (E2E) phantom test methodology. Five clinical SRS plans were mapped to an acrylic phantom containing a radiochromic film. The resulting phantom-based plans (E2E plans) were delivered four times. The phantom was setup on the treatment table with intentional misalignments, and CBCT-imaging was used to align it prior to E2E plan delivery. Comparisons (global gamma analysis) of the planned and delivered dose to the film were performed using a commercial triple-channel film dosimetry software. The necessary distance-to-agreement to achieve a 95% (DTA95) gamma passing rate for a fixed 3% dose difference provided an estimate of the spatial accuracy of CBCT-guided SRS. Systematic (∑) and random (σ) error components, as well as 95% confidence levels were derived for the DTA95 metric. The overall systematic spatial accuracy averaged over all tests was 1.4mm (SD: 0.2mm), with a corresponding 95% confidence level of 1.8mm. The systematic (Σ) and random (σ) spatial components of the accuracy derived from the E2E tests were 0.2mm and 0.8mm, respectively. The E2E methodology used in this study allowed an estimation of the spatial accuracy of our CBCT-guided SRS procedure. Subsequently, a PTV margin of 2.0mm is currently used in our department. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

A 3D THz image processing methodology for a fully integrated, semi-automatic and near real-time operational system

NASA Astrophysics Data System (ADS)

Brook, A.; Cristofani, E.; Vandewal, M.; Matheis, C.; Jonuscheit, J.; Beigang, R.

2012-05-01

The present study proposes a fully integrated, semi-automatic and near real-time mode-operated image processing methodology developed for Frequency-Modulated Continuous-Wave (FMCW) THz images with the center frequencies around: 100 GHz and 300 GHz. The quality control of aeronautics composite multi-layered materials and structures using Non-Destructive Testing is the main focus of this work. Image processing is applied on the 3-D images to extract useful information. The data is processed by extracting areas of interest. The detected areas are subjected to image analysis for more particular investigation managed by a spatial model. Finally, the post-processing stage examines and evaluates the spatial accuracy of the extracted information.

The basis function approach for modeling autocorrelation in ecological data.

PubMed

Hefley, Trevor J; Broms, Kristin M; Brost, Brian M; Buderman, Frances E; Kay, Shannon L; Scharf, Henry R; Tipton, John R; Williams, Perry J; Hooten, Mevin B

2017-03-01

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data. © 2016 by the Ecological Society of America.

Utilizing Weather RADAR for Rapid Location of Meteorite Falls and Space Debris Re-Entry

NASA Technical Reports Server (NTRS)

Fries, Marc D.

2016-01-01

This activity utilizes existing NOAA weather RADAR imagery to locate meteorite falls and space debris falls. The near-real-time availability and spatial accuracy of these data allow rapid recovery of material from both meteorite falls and space debris re-entry events. To date, at least 22 meteorite fall recoveries have benefitted from RADAR detection and fall modeling, and multiple debris re-entry events over the United States have been observed in unprecedented detail.

Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

NASA Technical Reports Server (NTRS)

Luther, S. G.; Bartolucci, L. A.; Hoffer, R. M.

1975-01-01

LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack.

The effect of lossy image compression on image classification

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1995-01-01

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.

Development of Early Warning System Using ALOS-2/PALSAR-2 Data to Detect and Prevent Deforestation

NASA Astrophysics Data System (ADS)

Hayashi, M.; Nagatani, I.; Watanabe, T.; Tadono, T.; Miyoshi, H.; Watanabe, M.; Koyama, C.; Shimada, M.; Ogawa, T.; Ishii, K.; Higashiuwatoko, T.; Miura, M.; Okonogi, H.; Adachi, K.; Morita, T.

2017-12-01

Satellite observation is an efficient method for monitoring deforestation, and a synthetic aperture radar (SAR) is useful especially in cloudy tropical forest regions. In this context, JICA and JAXA cooperate to operate the deforestation monitoring system acquired data by the Phased Array type L-band SAR-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), which is named as "JICA-JAXA Forest Early Warning System in the Tropics" (JJ-FAST), and it have been released on November 2016. JJ-FAST detects deforestation areas, and provides their positional information for 77 countries, which is covering almost all tropical forests. It uses PALSAR-2 ScanSAR observation mode (wide-observation swath width) image, which is 50 m spatial resolution acquired at 1.5 months interval. The dark change areas compared with in two acquisitions by PALSAR-2 HV-polarization images are identified as deforestations in the system. We conducted field surveys to validate detection accuracy of the JJ-FAST in Peru (November and December, 2016), Botswana (April, 2017), and Gabon (July, 2017). As the results, 15 of 18 detected areas were correct deforestation areas, therefore user's accuracy could be confirmed as 83.3 % from limited number of the validation data. Erroneous detection areas were caused by seasonal change in agricultural land and open burning in grass land. For improvement of the accuracy, such areas must be excluded from the analysis by additional algorithms e.g. estimation of accurate masking for non-forested areas. Therefore, we are revising the forest map used for pre-processing step in the system. The JJ-FAST can be expected to contribute to monitor and reduce illegal deforestation activities in tropical forests.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

PubMed Central

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID:26402522

Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content.

PubMed

Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang

2017-11-15

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discovering Structural Regularity in 3D Geometry

PubMed Central

Pauly, Mark; Mitra, Niloy J.; Wallner, Johannes; Pottmann, Helmut; Guibas, Leonidas J.

2010-01-01

We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or mesh-based models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis. PMID:21170292

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study.

PubMed

Yang, Ping; Fan, Chenggui; Wang, Min; Fogelson, Noa; Li, Ling

2017-08-15

Object identity and location are bound together to form a unique integration that is maintained and processed in visual working memory (VWM). Changes in task-irrelevant object location have been shown to impair the retrieval of memorial representations and the detection of object identity changes. However, the neural correlates of this cognitive process remain largely unknown. In the present study, we aim to investigate the underlying brain activation during object color change detection and the modulatory effects of changes in object location and VWM load. To this end we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings, which can reveal the neural activity with both high temporal and high spatial resolution. Subjects responded faster and with greater accuracy in the repeated compared to the changed object location condition, when a higher VWM load was utilized. These results support the spatial congruency advantage theory and suggest that it is more pronounced with higher VWM load. Furthermore, the spatial congruency effect was associated with larger posterior N1 activity, greater activation of the right inferior frontal gyrus (IFG) and less suppression of the right supramarginal gyrus (SMG), when object location was repeated compared to when it was changed. The ERP-fMRI integrative analysis demonstrated that the object location discrimination-related N1 component is generated in the right SMG. Copyright © 2017 Elsevier Inc. All rights reserved.

Influence of instrument conditions on the evaporation behavior of uranium dioxide with UV laser-assisted atom probe tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valderrama, B.; Henderson, H.B.; Gan, J.

2015-04-01

Atom probe tomography (APT) provides the ability to detect subnanometer chemical variations spatially, with high accuracy. However, it is known that compositional accuracy can be affected by experimental conditions. A study of the effect of laser energy, specimen base temperature, and detection rate is performed on the evaporation behavior of uranium dioxide (UO 2). In laser-assisted mode, tip geometry and standing voltage also contribute to the evaporation behavior. In this investigation, it was determined that modifying the detection rate and temperature did not affect the evaporation behavior as significantly as laser energy. It was also determined that three laser evaporationmore » regimes are present in UO 2. Very low laser energy produces a behavior similar to DC-field evaporation, moderate laser energy produces the desired laser-assisted field evaporation characteristic and high laser energy induces thermal effects, negatively altering the evaporation behavior. The need for UO 2 to be analyzed under moderate laser energies to produce accurate stoichiometry distinguishes it from other oxides. The following experimental conditions providing the best combination of mass resolving power, accurate stoichiometry, and uniform evaporation behavior: 50 K, 10 pJ laser energy, a detection rate of 0.003 atoms per pulse, and a 100 kHz repetition rate.« less

A tactile sensing element based on a hetero-core optical fiber for force measurement and texture detection

NASA Astrophysics Data System (ADS)

Yamazaki, Hiroshi; Koyama, Yuya; Watanabe, Kazuhiro

2014-05-01

Tactile sensing technology can measure a given property of an object through physical contact between a sensing element and the object. Various tactile sensing techniques have been developed for several applications such as intelligent robots, tactile interface, medical support and nursing care support. A desirable tactile sensing element for supporting human daily life can be embedded in the soft material with high sensitivity and accuracy in order to prevent from damaging to human or object physically. This report describes a new tactile sensing element. Hetero-core optical fibers have high sensitivity of macro-bending at local sensor portion and temperature independency, including advantages of optical fiber itself; thin size, light weight, flexible transmission line, and immunity to electro-magnetic interference. The proposed tactile sensing element could detect textures of touched objects through the optical loss caused by the force applied to the sensing element. The characteristics of the sensing element have been evaluated, in which the sensing element has the monotonic and non-linear sensitivity against the normal force ranged from 0 to 5 N with lower accuracy than 0.25 dB. Additionally, texture detection have been successfully demonstrated in which small surface figures of 0.1 mm in height were detected with spatial resolution of 0.4 mm.

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Developing collaborative classifiers using an expert-based model

USGS Publications Warehouse

Mountrakis, G.; Watts, R.; Luo, L.; Wang, Jingyuan

2009-01-01

This paper presents a hierarchical, multi-stage adaptive strategy for image classification. We iteratively apply various classification methods (e.g., decision trees, neural networks), identify regions of parametric and geographic space where accuracy is low, and in these regions, test and apply alternate methods repeating the process until the entire image is classified. Currently, classifiers are evaluated through human input using an expert-based system; therefore, this paper acts as the proof of concept for collaborative classifiers. Because we decompose the problem into smaller, more manageable sub-tasks, our classification exhibits increased flexibility compared to existing methods since classification methods are tailored to the idiosyncrasies of specific regions. A major benefit of our approach is its scalability and collaborative support since selected low-accuracy classifiers can be easily replaced with others without affecting classification accuracy in high accuracy areas. At each stage, we develop spatially explicit accuracy metrics that provide straightforward assessment of results by non-experts and point to areas that need algorithmic improvement or ancillary data. Our approach is demonstrated in the task of detecting impervious surface areas, an important indicator for human-induced alterations to the environment, using a 2001 Landsat scene from Las Vegas, Nevada. ?? 2009 American Society for Photogrammetry and Remote Sensing.

a Bottom-Up Geosptial Data Update Mechanism for Spatial Data Infrastructure Updating

NASA Astrophysics Data System (ADS)

Tian, W.; Zhu, X.; Liu, Y.

2012-08-01

Currently, the top-down spatial data update mechanism has made a big progress and it is wildly applied in many SDI (spatial data infrastructure). However, this mechanism still has some issues. For example, the update schedule is limited by the professional department's project, usually which is too long for the end-user; the data form collection to public cost too much time and energy for professional department; the details of geospatial information does not provide sufficient attribute, etc. Thus, how to deal with the problems has become the effective shortcut. Emerging Internet technology, 3S technique and geographic information knowledge which is popular in the public promote the booming development of geoscience in volunteered geospatial information. Volunteered geospatial information is the current "hotspot", which attracts many researchers to study its data quality and credibility, accuracy, sustainability, social benefit, application and so on. In addition to this, a few scholars also pay attention to the value of VGI to support the SDI updating. And on that basis, this paper presents a bottom-up update mechanism form VGI to SDI, which includes the processes of match homonymous elements between VGI and SDI vector data , change data detection, SDI spatial database update and new data product publication to end-users. Then, the proposed updating cycle is deeply discussed about the feasibility of which can detect the changed elements in time and shorten the update period, provide more accurate geometry and attribute data for spatial data infrastructure and support update propagation.

Accuracy of stream habitat interpolations across spatial scales

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2013-01-01

Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2 . Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2 ) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2 . Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled datasets.

Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

NASA Astrophysics Data System (ADS)

Erener, A.

2013-04-01

Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms.

Examining the Pilot and Controller Performance Data When in a Free Flight with Weather Phenomenon

NASA Technical Reports Server (NTRS)

Nituen, Celestine A.; Lozito, Sandra C. (Technical Monitor)

2002-01-01

The present study investigated effects of weather related factors on the performance of pilots under free flight. A weather scenario was defined by a combination of precipitation factors (light rain, moderate rain, and heavy rain or snow), visibility (1,4,8 miles), wind conditions (light, medium, or heavy), cloud ceiling (800ft. below, 1800ft above, and 4000ft horizontal). The performance of the aircraft self-separation was evaluated in terms of detection accuracy and detection times for student- and commercial (expert) pilots. Overall, the results obtained from a behavioral analysis showed that in general, the ability to recognize intruder aircraft conflict incidents, followed by the ability to acquire the spatial location of the intruder aircraft relative to ownership aircraft were judged to be the major cognitive tasks as perceived by the participants during self-separation. Further, the participants rarely used cockpit display of traffic information (CDTI) during conflict management related to aircraft separation, but used CDTI highly during decision-making tasks. In all weather scenarios, there were remarkable differences between expert and student pilots in detection times. In summary, weather scenarios were observed to affect intruder aircraft detection performance accuracies. There was interaction effects between weather Scenario-1 and Scenario-2 for climbing task data generated by both expert- and student- pilots at high traffic density. Scenario-3 weather condition provided an opportunity for poor detection accuracy as well as detection time increase. This may be attributed to low visibility. The intruder aircraft detection times were not affected by the weather conditions during climbing and descending tasks. The decision of pilots to fly into certain weather condition was dependent in part on the warning distance to the location of the weather. When pilots were warned of the weather conditions, they were more likely to fly their aircraft into it, but mostly when the warning was not close to the weather location.

Squids in the Study of Cerebral Magnetic Field

NASA Astrophysics Data System (ADS)

Romani, G. L.; Narici, L.

The following sections are included: * INTRODUCTION * HISTORICAL OVERVIEW * NEUROMAGNETIC FIELDS AND AMBIENT NOISE * DETECTORS * Room temperature sensors * SQUIDs * DETECTION COILS * Magnetometers * Gradiometers * Balancing * Planar gradiometers * Choice of the gradiometer parameters * MODELING * Current pattern due to neural excitations * Action potentials and postsynaptic currents * The current dipole model * Neural population and detected fields * Spherically bounded medium * SPATIAL CONFIGURATION OF THE SENSORS * SOURCE LOCALIZATION * Localization procedure * Experimental accuracy and reproducibility * SIGNAL PROCESSING * Analog Filtering * Bandpass filters * Line rejection filters * DATA ANALYSIS * Analysis of evoked/event-related responses * Simple average * Selected average * Recursive techniques * Similarity analysis * Analysis of spontaneous activity * Mapping and localization * EXAMPLES OF NEUROMAGNETIC STUDIES * Neuromagnetic measurements * Studies on the normal brain * Clinical applications * Epilepsy * Tinnitus * CONCLUSIONS * ACKNOWLEDGEMENTS * REFERENCES

Age-related similarities and differences in monitoring spatial cognition.

PubMed

Ariel, Robert; Moffat, Scott D

2018-05-01

Spatial cognitive performance is impaired in later adulthood but it is unclear whether the metacognitive processes involved in monitoring spatial cognitive performance are also compromised. Inaccurate monitoring could affect whether people choose to engage in tasks that require spatial thinking and also the strategies they use in spatial domains such as navigation. The current experiment examined potential age differences in monitoring spatial cognitive performance in a variety of spatial domains including visual-spatial working memory, spatial orientation, spatial visualization, navigation, and place learning. Younger and older adults completed a 2D mental rotation test, 3D mental rotation test, paper folding test, spatial memory span test, two virtual navigation tasks, and a cognitive mapping test. Participants also made metacognitive judgments of performance (confidence judgments, judgments of learning, or navigation time estimates) on each trial for all spatial tasks. Preference for allocentric or egocentric navigation strategies was also measured. Overall, performance was poorer and confidence in performance was lower for older adults than younger adults. In most spatial domains, the absolute and relative accuracy of metacognitive judgments was equivalent for both age groups. However, age differences in monitoring accuracy (specifically relative accuracy) emerged in spatial tasks involving navigation. Confidence in navigating for a target location also mediated age differences in allocentric navigation strategy use. These findings suggest that with the possible exception of navigation monitoring, spatial cognition may be spared from age-related decline even though spatial cognition itself is impaired in older age.

Waterbodies Extraction from LANDSAT8-OLI Imagery Using Awater Indexs-Guied Stochastic Fully-Connected Conditional Random Field Model and the Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, X.; Xu, L.

2018-04-01

One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method's performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

How Well Will MODIS Measure Top of Atmosphere Aerosol Direct Radiative Forcing?

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Kaufman, Yoram J.; Levin, Zev; Ghan, Stephen; Einaudi, Franco (Technical Monitor)

2000-01-01

The new generation of satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) will be able to detect and characterize global aerosols with an unprecedented accuracy. The question remains whether this accuracy will be sufficient to narrow the uncertainties in our estimates of aerosol radiative forcing at the top of the atmosphere. Satellite remote sensing detects aerosol optical thickness with the least amount of relative error when aerosol loading is high. Satellites are less effective when aerosol loading is low. We use the monthly mean results of two global aerosol transport models to simulate the spatial distribution of smoke aerosol in the Southern Hemisphere during the tropical biomass burning season. This spatial distribution allows us to determine that 87-94% of the smoke aerosol forcing at the top of the atmosphere occurs in grid squares with sufficient signal to noise ratio to be detectable from space. The uncertainty of quantifying the smoke aerosol forcing in the Southern Hemisphere depends on the uncertainty introduced by errors in estimating the background aerosol, errors resulting from uncertainties in surface properties and errors resulting from uncertainties in assumptions of aerosol properties. These three errors combine to give overall uncertainties of 1.5 to 2.2 Wm-2 (21-56%) in determining the Southern Hemisphere smoke aerosol forcing at the top of the atmosphere. The range of values depend on which estimate of MODIS retrieval uncertainty is used, either the theoretical calculation (upper bound) or the empirical estimate (lower bound). Strategies that use the satellite data to derive flux directly or use the data in conjunction with ground-based remote sensing and aerosol transport models can reduce these uncertainties.

Mapping and improving frequency, accuracy, and interpretation of land cover change: Classifying coastal Louisiana with 1990, 1993, 1996, and 1999 Landsat Thematic Mapper image data

USGS Publications Warehouse

Nelson, G.; Ramsey, Elijah W.; Rangoonwala, A.

2005-01-01

Landsat Thematic Mapper images and collateral data sources were used to classify the land cover of the Mermentau River Basin within the chenier coastal plain and the adjacent uplands of Louisiana, USA. Landcover classes followed that of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods needed to be developed to meet these national standards. Our first classification was limited to the Mermentau River Basin (MRB) in southcentral Louisiana, and the years of 1990, 1993, and 1996. To overcome problems due to class spectral inseparable, spatial and spectra continuums, mixed landcovers, and abnormal transitions, we separated the coastal area into regions of commonality and applying masks to specific land mixtures. Over the three years and 14 landcover classes (aggregating the cultivated land and grassland, and water and floating vegetation classes), overall accuracies ranged from 82% to 90%. To enhance landcover change interpretation, three indicators were introduced as Location Stability, Residence stability, and Turnover. Implementing methods substantiated in the multiple date MRB classification, we spatially extended the classification to the entire Louisiana coast and temporally extended the original 1990, 1993, 1996 classifications to 1999 (Figure 1). We also advanced the operational functionality of the classification and increased the credibility of change detection results. Increased operational functionality that resulted in diminished user input was for the most part gained by implementing a classification logic based on forbidden transitions. The logic detected and corrected misclassifications and mostly alleviated the necessity of subregion separation prior to the classification. The new methods provided an improved ability for more timely detection and response to landcover impact. ?? 2005 IEEE.

Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia

NASA Astrophysics Data System (ADS)

Dutrieux, Loïc Paul; Verbesselt, Jan; Kooistra, Lammert; Herold, Martin

2015-09-01

Automatically detecting forest disturbances as they occur can be extremely challenging for certain types of environments, particularly those presenting strong natural variations. Here, we use a generic structural break detection framework (BFAST) to improve the monitoring of forest cover loss by combining multiple data streams. Forest change monitoring is performed using Landsat data in combination with MODIS or rainfall data to further improve the modelling and monitoring. We tested the use of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) with varying spatial aggregation window sizes as well as a rainfall derived index as external regressors. The method was evaluated on a dry tropical forest area in lowland Bolivia where forest cover loss is known to occur, and we validated the results against a set of ground truth samples manually interpreted using the TimeSync environment. We found that the addition of an external regressor allows to take advantage of the difference in spatial extent between human induced and naturally induced variations and only detect the processes of interest. Of all configurations, we found the 13 by 13 km MODIS NDVI window to be the most successful, with an overall accuracy of 87%. Compared with a single pixel approach, the proposed method produced better time-series model fits resulting in increases of overall accuracy (from 82% to 87%), and decrease in omission and commission errors (from 33% to 24% and from 3% to 0% respectively). The presented approach seems particularly relevant for areas with high inter-annual natural variability, such as forests regularly experiencing exceptional drought events.

Advances of a Brillouin Scattering Lidar System for the Detection of Temperature Profiles in the Ocean: Laboratory Measurements and Field Test

NASA Astrophysics Data System (ADS)

Walther, T.; Rupp, D.; Friman, S.; Trees, C.; Fournier, G.

2016-02-01

Recently we have demonstrated the feasibility of remotely measuring temperature profiles in water under a laboratory environment employing our real-time Brillouin Scattering LIDAR (BSL) system. The working principle is based on the frequency and time resolved detection of the backscattered spontaneous Brillouin signal of a short light pulse fired into the ocean. The light source consists of a frequency-doubled fiber-amplified External Cavity Diode Laser (ECDL) providing high-energy, Fourier transform-limited laser pulses in the green spectral range. The Brillouin shift is detected with high accuracy (low uncertainty) by employing an edge filter based on an Excited State Faraday Anomalous Dispersion Optical Filter (ESFADOF). Time-resolution allows for the depth resolution and the frequency resolved shift is proportional to the speed of sound. Thus, the temperature profile can be extracted from the measurements. In our laboratory setup we were able to resolve water temperatures with a mean accuracy of up to 0.07 oC and a spatial resolution of 1 m depending on the amount of averaging. In order to prepare the system for a first field test under realistic conditions on the coast of the Mediterranean at CMRE in La Spezia, almost all of the components have been upgraded. This first test is planned for November 2015. We will present the above mentioned measurements, details about the upgrades and report on our experiences during this maritime field test.Ultimately, the plan is to operate the system from a mobile platform, e.g., a helicopter or vessel, in order to precisely determine the temperature of the surface mixed layer of the ocean with high spatial resolution.

Improving Landslide Susceptibility Modeling Using an Empirical Threshold Scheme for Excluding Landslide Deposition

NASA Astrophysics Data System (ADS)

Tsai, F.; Lai, J. S.; Chiang, S. H.

2015-12-01

Landslides are frequently triggered by typhoons and earthquakes in Taiwan, causing serious economic losses and human casualties. Remotely sensed images and geo-spatial data consisting of land-cover and environmental information have been widely used for producing landslide inventories and causative factors for slope stability analysis. Landslide susceptibility, on the other hand, can represent the spatial likelihood of landslide occurrence and is an important basis for landslide risk assessment. As multi-temporal satellite images become popular and affordable, they are commonly used to generate landslide inventories for subsequent analysis. However, it is usually difficult to distinguish different landslide sub-regions (scarp, debris flow, deposition etc.) directly from remote sensing imagery. Consequently, the extracted landslide extents using image-based visual interpretation and automatic detections may contain many depositions that may reduce the fidelity of the landslide susceptibility model. This study developed an empirical thresholding scheme based on terrain characteristics for eliminating depositions from detected landslide areas to improve landslide susceptibility modeling. In this study, Bayesian network classifier is utilized to build a landslide susceptibility model and to predict sequent rainfall-induced shallow landslides in the Shimen reservoir watershed located in northern Taiwan. Eleven causative factors are considered, including terrain slope, aspect, curvature, elevation, geology, land-use, NDVI, soil, distance to fault, river and road. Landslide areas detected using satellite images acquired before and after eight typhoons between 2004 to 2008 are collected as the main inventory for training and verification. In the analysis, previous landslide events are used as training data to predict the samples of the next event. The results are then compared with recorded landslide areas in the inventory to evaluate the accuracy. Experimental results demonstrate that the accuracies of landslide susceptibility analysis in all sequential predictions have been improved significantly after eliminating landslide depositions.

A Novel Multi-Digital Camera System Based on Tilt-Shift Photography Technology

PubMed Central

Sun, Tao; Fang, Jun-yong; Zhao, Dong; Liu, Xue; Tong, Qing-xi

2015-01-01

Multi-digital camera systems (MDCS) are constantly being improved to meet the increasing requirement of high-resolution spatial data. This study identifies the insufficiencies of traditional MDCSs and proposes a new category MDCS based on tilt-shift photography to improve ability of the MDCS to acquire high-accuracy spatial data. A prototype system, including two or four tilt-shift cameras (TSC, camera model: Nikon D90), is developed to validate the feasibility and correctness of proposed MDCS. Similar to the cameras of traditional MDCSs, calibration is also essential for TSC of new MDCS. The study constructs indoor control fields and proposes appropriate calibration methods for TSC, including digital distortion model (DDM) approach and two-step calibrated strategy. The characteristics of TSC are analyzed in detail via a calibration experiment; for example, the edge distortion of TSC. Finally, the ability of the new MDCS to acquire high-accuracy spatial data is verified through flight experiments. The results of flight experiments illustrate that geo-position accuracy of prototype system achieves 0.3 m at a flight height of 800 m, and spatial resolution of 0.15 m. In addition, results of the comparison between the traditional (MADC II) and proposed MDCS demonstrate that the latter (0.3 m) provides spatial data with higher accuracy than the former (only 0.6 m) under the same conditions. We also take the attitude that using higher accuracy TSC in the new MDCS should further improve the accuracy of the photogrammetry senior product. PMID:25835187

A novel multi-digital camera system based on tilt-shift photography technology.

PubMed

Sun, Tao; Fang, Jun-Yong; Zhao, Dong; Liu, Xue; Tong, Qing-Xi

2015-03-31

Multi-digital camera systems (MDCS) are constantly being improved to meet the increasing requirement of high-resolution spatial data. This study identifies the insufficiencies of traditional MDCSs and proposes a new category MDCS based on tilt-shift photography to improve ability of the MDCS to acquire high-accuracy spatial data. A prototype system, including two or four tilt-shift cameras (TSC, camera model: Nikon D90), is developed to validate the feasibility and correctness of proposed MDCS. Similar to the cameras of traditional MDCSs, calibration is also essential for TSC of new MDCS. The study constructs indoor control fields and proposes appropriate calibration methods for TSC, including digital distortion model (DDM) approach and two-step calibrated strategy. The characteristics of TSC are analyzed in detail via a calibration experiment; for example, the edge distortion of TSC. Finally, the ability of the new MDCS to acquire high-accuracy spatial data is verified through flight experiments. The results of flight experiments illustrate that geo-position accuracy of prototype system achieves 0.3 m at a flight height of 800 m, and spatial resolution of 0.15 m. In addition, results of the comparison between the traditional (MADC II) and proposed MDCS demonstrate that the latter (0.3 m) provides spatial data with higher accuracy than the former (only 0.6 m) under the same conditions. We also take the attitude that using higher accuracy TSC in the new MDCS should further improve the accuracy of the photogrammetry senior product.

Assessment of changes in formations of non-forest woody vegetation in southern Denmark based on airborne LiDAR.

PubMed

Angelidis, Ioannis; Levin, Gregor; Díaz-Varela, Ramón Alberto; Malinowski, Radek

2017-09-01

LiDAR (Light Detection and Ranging) is a remote sensing technology that uses light in the form of pulses to measure the range between a sensor and the Earth's surface. Recent increase in availability of airborne LiDAR scanning (ALS) data providing national coverage with high point densities has opened a wide range of possibilities for monitoring landscape elements and their changes at broad geographical extent. We assessed the dynamics of the spatial extent of non-forest woody vegetation (NFW) in a study area of approx. 2500 km 2 in southern Jutland, Denmark, based on two acquisitions of ALS data for 2006 and 2014 in combination with other spatial data. Our results show a net-increase (4.8%) in the total area of NFW. Furthermore, this net change comprises of both areas with a decrease and areas with an increase of NFW. An accuracy assessment based on visual interpretation of aerial photos indicates high accuracy (>95%) in the delineation of NFW without changes during the study period. For NFW that changed between 2006 and 2014, accuracies were lower (90 and 82% in removed and new features, respectively), which is probably due to lower point densities of the 2006 ALS data (0.5 pts./m 2 ) compared to the 2014 data (4-5 pts./m 2 ). We conclude that ALS data, if combined with other spatial data, in principle are highly suitable for detailed assessment of changes in landscape features, such as formations of NFW at broad geographical extent. However, in change assessment based on multi-temporal ALS data with different point densities errors occur, particularly when examining small or narrow NFW objects.

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall.

PubMed

Crespo-García, Maité; Zeiller, Monika; Leupold, Claudia; Kreiselmeyer, Gernot; Rampp, Stefan; Hamer, Hajo M; Dalal, Sarang S

2016-11-15

Human hippocampal theta oscillations play a key role in accurate spatial coding. Associative encoding involves similar hippocampal networks but, paradoxically, is also characterized by theta power decreases. Here, we investigated how theta activity relates to associative encoding of place contexts resulting in accurate navigation. Using MEG, we found that slow-theta (2-5Hz) power negatively correlated with subsequent spatial accuracy for virtual contextual locations in posterior hippocampus and other cortical structures involved in spatial cognition. A rare opportunity to simultaneously record MEG and intracranial EEG in an epilepsy patient provided crucial insights: during power decreases, slow-theta in right anterior hippocampus and left inferior frontal gyrus phase-led the left temporal cortex and predicted spatial accuracy. Our findings indicate that decreased slow-theta activity reflects local and long-range neural mechanisms that encode accurate spatial contexts, and strengthens the view that local suppression of low-frequency activity is essential for more efficient processing of detailed information. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparisons of GLM and LMA Observations

NASA Astrophysics Data System (ADS)

Thomas, R. J.; Krehbiel, P. R.; Rison, W.; Stanley, M. A.; Attanasio, A.

2017-12-01

Observations from 3-dimensional VHF lightning mapping arrays (LMAs) provide a valuable basis for evaluating the spatial accuracy and detection efficiencies of observations from the recently launched, optical-based Geosynchronous Lightning Mapper (GLM). In this presentation, we describe results of comparing the LMA and GLM observations. First, the observations are compared spatially and temporally at the individual event (pixel) level for sets of individual discharges. For LMA networks in Florida, Colorado, and Oklahoma, the GLM observations are well correlated time-wise with LMA observations but are systematically offset by one- to two pixels ( 10 to 15 or 20 km) in a southwesterly direction from the actual lightning activity. The graphical comparisons show a similar location uncertainty depending on the altitude at which the scattered light is emitted from the parent cloud, due to being observed at slant ranges. Detection efficiencies (DEs) can be accurately determined graphically for intervals where individual flashes in a storm are resolved time-wise, and DEs and false alarm rates can be automated using flash sorting algorithms for overall and/or larger storms. This can be done as a function of flash size and duration, and generally shows high detection rates for larger flashes. Preliminary results during the May 1 2017 ER-2 overflight of Colorado storms indicate decreased detection efficiency if the storm is obscured by an overlying cloud layer.

Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

PubMed Central

Lu, Xiaofeng; Song, Li; Shen, Sumin; He, Kang; Yu, Songyu; Ling, Nam

2013-01-01

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness. PMID:23867746

Parallel Hough Transform-based straight line detection and its FPGA implementation in embedded vision.

PubMed

Lu, Xiaofeng; Song, Li; Shen, Sumin; He, Kang; Yu, Songyu; Ling, Nam

2013-07-17

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness.

Spatial adaptation procedures on tetrahedral meshes for unsteady aerodynamic flow calculations

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Batina, John T.; Yang, Henry T. Y.

1993-01-01

Spatial adaptation procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaptation procedures were developed and implemented within a three-dimensional, unstructured-grid, upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in high gradient regions of the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational cost. A detailed description of the enrichment and coarsening procedures are presented and comparisons with experimental data for an ONERA M6 wing and an exact solution for a shock-tube problem are presented to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady results, obtained using spatial adaptation procedures, are shown to be of high spatial accuracy, primarily in that discontinuities such as shock waves are captured very sharply.

Spatial adaptation procedures on tetrahedral meshes for unsteady aerodynamic flow calculations

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Batina, John T.; Yang, Henry T. Y.

1993-01-01

Spatial adaptation procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaptation procedures were developed and implemented within a three-dimensional, unstructured-grid, upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in high gradient regions of the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational cost. The paper gives a detailed description of the enrichment and coarsening procedures and presents comparisons with experimental data for an ONERA M6 wing and an exact solution for a shock-tube problem to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady results, obtained using spatial adaptation procedures, are shown to be of high spatial accuracy, primarily in that discontinuities such as shock waves are captured very sharply.

The Role of Standardized and Study-specific Human Brain Diffusion Tensor Templates in Inter-subject Spatial Normalization

PubMed Central

Zhang, Shengwei; Arfanakis, Konstantinos

2012-01-01

Purpose To investigate the effect of standardized and study-specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness. Materials and Methods Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study-specific templates, using three registration approaches. The accuracy of inter-subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data. Results It was demonstrated that, for DTI data with visible artifacts, the study-specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study-specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy. Conclusion For DTI data with visible artifacts, a carefully constructed study-specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high-quality standardized template may be more advantageous than a study-specific template, since in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels. PMID:23034880

High-resolution myocardial stress perfusion at 3 T in patients with suspected coronary artery disease.

PubMed

Meyer, Carsten; Strach, Katharina; Thomas, Daniel; Litt, Harold; Nähle, Claas P; Tiemann, Klaus; Schwenger, Ulrich; Schild, Hans H; Sommer, Torsten

2008-02-01

To implement a high-resolution first-pass myocardial perfusion imaging protocol (HRPI) at 3 T, and to evaluate the feasibility, image quality and accuracy of this approach prospectively in patients with suspected CAD. We hypothesized that utilizing the gain in SNR at 3 T to increase spatial resolution would reduce partial volume effects and subendocardial dark rim artifacts in comparison to 1.5 T. HRPI studies were performed on 60 patients using a segmented k-space gradient echo sequence (in plane resolution 1.97 x 1.94 mm(2)). Semiquantitative assessment of dark rim artifacts was performed for the stress studies on a slice-by-slice basis. Qualitative visual analysis was compared to quantitative coronary angiography (QCA) results; hemodynamically significant CAD was defined as stenosis >or=70% at QCA. Dark rim artifacts appeared in 108 of 180 slices (average extent 1.3 +/- 1.2 mm representing 11.8 +/- 10.8% of the transmural myocardial thickness). Sensitivity, specifity, and test accuracy for the detection of significant CAD were 89%,79%, and 85%. HRPI studies at 3 T are feasible in a clinical setting, providing good image quality and high accuracy for detection of significant CAD. The presence of dark rim artifacts does not appear to represent a diagnostic problem when using a HRPI approach.

Detection of mechanical injury on pickling cucumbers using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Ariana, D.; Lu, R.; Guyer, D.

2005-11-01

Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.

Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization

NASA Astrophysics Data System (ADS)

Li, Linyi; Chen, Yun; Yu, Xin; Liu, Rui; Huang, Chang

2015-03-01

The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM + images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.

Prospects for higher spatial resolution quantitative X-ray analysis using transition element L-lines

NASA Astrophysics Data System (ADS)

Statham, P.; Holland, J.

2014-03-01

Lowering electron beam kV reduces electron scattering and improves spatial resolution of X-ray analysis. However, a previous round robin analysis of steels at 5 - 6 kV using Lα-lines for the first row transition elements gave poor accuracies. Our experiments on SS63 steel using Lα-lines show similar biases in Cr and Ni that cannot be corrected with changes to self-absorption coefficients or carbon coating. The inaccuracy may be caused by different probabilities for emission and anomalous self-absorption for the La-line between specimen and pure element standard. Analysis using Ll(L3-M1)-lines gives more accurate results for SS63 plausibly because the M1-shell is not so vulnerable to the atomic environment as the unfilled M4,5-shell. However, Ll-intensities are very weak and WDS analysis may be impractical for some applications. EDS with large area SDD offers orders of magnitude faster analysis and achieves similar results to WDS analysis with Lα-lines but poorer energy resolution precludes the use of Ll-lines in most situations. EDS analysis of K-lines at low overvoltage is an alternative strategy for improving spatial resolution that could give higher accuracy. The trade-off between low kV versus low overvoltage is explored in terms of sensitivity for element detection for different elements.

Rapid broad area search and detection of Chinese surface-to-air missile sites using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Marcum, Richard A.; Davis, Curt H.; Scott, Grant J.; Nivin, Tyler W.

2017-10-01

We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth's surface. Various DCNN were trained and tested using fewer than 100 positive training examples (China only) from a worldwide surface-to-air-missile (SAM) site dataset. A ResNet-101 DCNN achieved a 98.2% average accuracy for the China SAM site data. The ResNet-101 DCNN was used to process ˜19.6 M image chips over a large study area in southeastern China. DCNN chip detections (˜9300) were postprocessed with a spatial clustering algorithm to produce a ranked list of ˜2100 candidate SAM site locations. The combination of DCNN processing and spatial clustering effectively reduced the search area by ˜660X (0.15% of the DCNN-processed land area). An efficient web interface was used to facilitate a rapid serial human review of the candidate SAM sites in the China study area. Four novice imagery analysts with no prior imagery analysis experience were able to complete a DCNN-assisted SAM site search in an average time of ˜42 min. This search was ˜81X faster than a traditional visual search over an equivalent land area of ˜88,640 km2 while achieving nearly identical statistical accuracy (˜90% F1).

MR-based source localization for MR-guided HDR brachytherapy

NASA Astrophysics Data System (ADS)

Beld, E.; Moerland, M. A.; Zijlstra, F.; Viergever, M. A.; Lagendijk, J. J. W.; Seevinck, P. R.

2018-04-01

For the purpose of MR-guided high-dose-rate (HDR) brachytherapy, a method for real-time localization of an HDR brachytherapy source was developed, which requires high spatial and temporal resolutions. MR-based localization of an HDR source serves two main aims. First, it enables real-time treatment verification by determination of the HDR source positions during treatment. Second, when using a dummy source, MR-based source localization provides an automatic detection of the source dwell positions after catheter insertion, allowing elimination of the catheter reconstruction procedure. Localization of the HDR source was conducted by simulation of the MR artifacts, followed by a phase correlation localization algorithm applied to the MR images and the simulated images, to determine the position of the HDR source in the MR images. To increase the temporal resolution of the MR acquisition, the spatial resolution was decreased, and a subpixel localization operation was introduced. Furthermore, parallel imaging (sensitivity encoding) was applied to further decrease the MR scan time. The localization method was validated by a comparison with CT, and the accuracy and precision were investigated. The results demonstrated that the described method could be used to determine the HDR source position with a high accuracy (0.4–0.6 mm) and a high precision (⩽0.1 mm), at high temporal resolutions (0.15–1.2 s per slice). This would enable real-time treatment verification as well as an automatic detection of the source dwell positions.

Integrating conflict detection and attentional control mechanisms.

PubMed

Walsh, Bong J; Buonocore, Michael H; Carter, Cameron S; Mangun, George R

2011-09-01

Human behavior involves monitoring and adjusting performance to meet established goals. Performance-monitoring systems that act by detecting conflict in stimulus and response processing have been hypothesized to influence cortical control systems to adjust and improve performance. Here we used fMRI to investigate the neural mechanisms of conflict monitoring and resolution during voluntary spatial attention. We tested the hypothesis that the ACC would be sensitive to conflict during attentional orienting and influence activity in the frontoparietal attentional control network that selectively modulates visual information processing. We found that activity in ACC increased monotonically with increasing attentional conflict. This increased conflict detection activity was correlated with both increased activity in the attentional control network and improved speed and accuracy from one trial to the next. These results establish a long hypothesized interaction between conflict detection systems and neural systems supporting voluntary control of visual attention.

Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoegele, W.; Loeschel, R.; Dobler, B.

2011-02-15

Purpose: In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. Methods: The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem withmore » prior knowledge and is solved using a Bayesian maximum a posteriori (MAP) approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup (such as in-room laser and skin marks), the plasticity of the body (relative shifts between target and features), and the feature detection error in CB projections (which may vary depending on specific detection algorithm and feature type). For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge (initial setup accuracy, plasticity of the body, and number of features) and the parameters of CB data acquisition (number of projections and accuracy of feature detection) to the expected estimation accuracy. Results: MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections (XVI, Elekta Synergy) by a local threshold method for demonstration purposes only. The accuracy of estimation (strongly varying for different plasticity parameters of the body) agreed with the rule of thumb formula. Moreover, based on this rule of thumb formula, about 20 projections for 6 detectable features seem to be sufficient for a target estimation accuracy of 0.2 cm, even for relatively large feature detection errors with standard deviation of 0.5 cm and spatial displacements of the features with standard deviation of 0.5 cm. Conclusions: The authors have introduced a general MAP-based patient setup algorithm accounting for different sources of uncertainties, which are utilized as the prior knowledge in a transparent way. This new framework can be further utilized for different clinical sites, as well as theoretical developments in the field of patient positioning for radiotherapy.« less

Comparison of Arterial Spin-labeling Perfusion Images at Different Spatial Normalization Methods Based on Voxel-based Statistical Analysis.

PubMed

Tani, Kazuki; Mio, Motohira; Toyofuku, Tatsuo; Kato, Shinichi; Masumoto, Tomoya; Ijichi, Tetsuya; Matsushima, Masatoshi; Morimoto, Shoichi; Hirata, Takumi

2017-01-01

Spatial normalization is a significant image pre-processing operation in statistical parametric mapping (SPM) analysis. The purpose of this study was to clarify the optimal method of spatial normalization for improving diagnostic accuracy in SPM analysis of arterial spin-labeling (ASL) perfusion images. We evaluated the SPM results of five spatial normalization methods obtained by comparing patients with Alzheimer's disease or normal pressure hydrocephalus complicated with dementia and cognitively healthy subjects. We used the following methods: 3DT1-conventional based on spatial normalization using anatomical images; 3DT1-DARTEL based on spatial normalization with DARTEL using anatomical images; 3DT1-conventional template and 3DT1-DARTEL template, created by averaging cognitively healthy subjects spatially normalized using the above methods; and ASL-DARTEL template created by averaging cognitively healthy subjects spatially normalized with DARTEL using ASL images only. Our results showed that ASL-DARTEL template was small compared with the other two templates. Our SPM results obtained with ASL-DARTEL template method were inaccurate. Also, there were no significant differences between 3DT1-conventional and 3DT1-DARTEL template methods. In contrast, the 3DT1-DARTEL method showed higher detection sensitivity, and precise anatomical location. Our SPM results suggest that we should perform spatial normalization with DARTEL using anatomical images.

AMES Stereo Pipeline Derived DEM Accuracy Experiment Using LROC-NAC Stereopairs and Weighted Spatial Dependence Simulation for Lunar Site Selection

NASA Astrophysics Data System (ADS)

Laura, J. R.; Miller, D.; Paul, M. V.

2012-03-01

An accuracy assessment of AMES Stereo Pipeline derived DEMs for lunar site selection using weighted spatial dependence simulation and a call for outside AMES derived DEMs to facilitate a statistical precision analysis.

Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion

NASA Astrophysics Data System (ADS)

Fu, Shichen; Li, Yiming; Zhang, Minjun; Zong, Kai; Cheng, Long; Wu, Miao

2018-01-01

To realize unmanned pose detection of a coalmine boom-type roadheader, an ultra-wideband (UWB) pose detection system (UPDS) for a roadheader is designed, which consists of four UWB positioning base stations and three roadheader positioning nodes. The positioning base stations are used in turn to locate the positioning nodes of the roadheader fuselage. Using 12 sets of distance measurement information, a time-of-arrival (TOA) positioning model is established to calculate the 3D coordinates of three positioning nodes of the roadheader fuselage, and the three attitude angles (heading, pitch, and roll angles) of the roadheader fuselage are solved. A range accuracy experiment of a UWB P440 module was carried out in a narrow and closed tunnel, and the experiment data show that the mean error and standard deviation of the module can reach below 2 cm. Based on the TOA positioning model of the UPDS, we propose a fusion-positioning algorithm based on a Caffery transform and Taylor series expansion (CTFPA). We derived the complete calculation process, designed a flowchart, and carried out a simulation of CTFPA in MATLAB, comparing 1000 simulated positioning nodes of CTFPA and the Caffery positioning algorithm (CPA) for a 95 m long tunnel. The positioning error field of the tunnel was established, and the influence of the spatial variation on the positioning accuracy of CPA and CTFPA was analysed. The simulation results show that, compared with CPA, the positioning accuracy of CTFPA is clearly improved, and the accuracy of each axis can reach more than 5 mm. The accuracy of the X-axis is higher than that of the Y- and Z-axes. In section X-Y of the tunnel, the root mean square error (RMSE) contours of CTFPA are clear and orderly, and with an increase in the measuring distance, RMSE increases linearly. In section X-Z, the RMSE contours are concentric circles, and the variation ratio is nonlinear.

Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada

NASA Astrophysics Data System (ADS)

Zhang, Kongwen; Hu, Baoxin; Robinson, Justin

2014-01-01

The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.

Detection and Counting of Orchard Trees from Vhr Images Using a Geometrical-Optical Model and Marked Template Matching

NASA Astrophysics Data System (ADS)

Maillard, Philippe; Gomes, Marília F.

2016-06-01

This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the "template matching" image processing approach, in which the template is based on a "geometricaloptical" model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications. The algorithm is tested on four images from different regions of the world and different crop types. These images all have < 1 meter spatial resolution and were downloaded from the GoogleEarth application. Results show that the algorithm is very efficient at detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required parameters is under development.

Detecting understory plant invasion in urban forests using LiDAR

NASA Astrophysics Data System (ADS)

Singh, Kunwar K.; Davis, Amy J.; Meentemeyer, Ross K.

2015-06-01

Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features - optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

Influence of Spatial Resolution in Three-dimensional Cine Phase Contrast Magnetic Resonance Imaging on the Accuracy of Hemodynamic Analysis

PubMed Central

Fukuyama, Atsushi; Isoda, Haruo; Morita, Kento; Mori, Marika; Watanabe, Tomoya; Ishiguro, Kenta; Komori, Yoshiaki; Kosugi, Takafumi

2017-01-01

Introduction: We aim to elucidate the effect of spatial resolution of three-dimensional cine phase contrast magnetic resonance (3D cine PC MR) imaging on the accuracy of the blood flow analysis, and examine the optimal setting for spatial resolution using flow phantoms. Materials and Methods: The flow phantom has five types of acrylic pipes that represent human blood vessels (inner diameters: 15, 12, 9, 6, and 3 mm). The pipes were fixed with 1% agarose containing 0.025 mol/L gadolinium contrast agent. A blood-mimicking fluid with human blood property values was circulated through the pipes at a steady flow. Magnetic resonance (MR) images (three-directional phase images with speed information and magnitude images for information of shape) were acquired using the 3-Tesla MR system and receiving coil. Temporal changes in spatially-averaged velocity and maximum velocity were calculated using hemodynamic analysis software. We calculated the error rates of the flow velocities based on the volume flow rates measured with a flowmeter and examined measurement accuracy. Results: When the acrylic pipe was the size of the thoracicoabdominal or cervical artery and the ratio of pixel size for the pipe was set at 30% or lower, spatially-averaged velocity measurements were highly accurate. When the pixel size ratio was set at 10% or lower, maximum velocity could be measured with high accuracy. It was difficult to accurately measure maximum velocity of the 3-mm pipe, which was the size of an intracranial major artery, but the error for spatially-averaged velocity was 20% or less. Conclusions: Flow velocity measurement accuracy of 3D cine PC MR imaging for pipes with inner sizes equivalent to vessels in the cervical and thoracicoabdominal arteries is good. The flow velocity accuracy for the pipe with a 3-mm-diameter that is equivalent to major intracranial arteries is poor for maximum velocity, but it is relatively good for spatially-averaged velocity. PMID:28132996

Accuracy assessment of seven global land cover datasets over China

NASA Astrophysics Data System (ADS)

Yang, Yongke; Xiao, Pengfeng; Feng, Xuezhi; Li, Haixing

2017-03-01

Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special attention should be paid for data users who are interested in these regions.

Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography

NASA Astrophysics Data System (ADS)

Boudria, Yacine; Feltane, Amal; Besio, Walter

2014-06-01

Objective. Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements. Approach. Eight human subjects were asked to imagine either ‘left’ or ‘right’ hand movement during one recording session to control the computer cursor using TCRE and disc electrodes. Main results. The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%). Significance. This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

Accurate, reliable prototype earth horizon sensor head

NASA Technical Reports Server (NTRS)

Schwarz, F.; Cohen, H.

1973-01-01

The design and performance is described of an accurate and reliable prototype earth sensor head (ARPESH). The ARPESH employs a detection logic 'locator' concept and horizon sensor mechanization which should lead to high accuracy horizon sensing that is minimally degraded by spatial or temporal variations in sensing attitude from a satellite in orbit around the earth at altitudes in the 500 km environ 1,2. An accuracy of horizon location to within 0.7 km has been predicted, independent of meteorological conditions. This corresponds to an error of 0.015 deg-at 500 km altitude. Laboratory evaluation of the sensor indicates that this accuracy is achieved. First, the basic operating principles of ARPESH are described; next, detailed design and construction data is presented and then performance of the sensor under laboratory conditions in which the sensor is installed in a simulator that permits it to scan over a blackbody source against background representing the earth space interface for various equivalent plant temperatures.

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

The theory of double edge lidar techniques for measuring the atmospheric wind using aerosol and molecular backscatter is described. Two high spectral resolution filters with opposite slopes are located about the laser frequency for the aerosol based measurement or in the wings of the Rayleigh - Brillouin profile for the molecular measurement. This doubles the signal change per unit Doppler shift and improves the measurement accuracy by nearly a factor of 2 relative to the single edge technique. For the aerosol based measurement, the use of two high resolution edge filters reduces the effects of background, Rayleigh scattering, by as much as an order of magnitude and substantially improves the measurement accuracy. Also, we describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined. A measurement accuracy of 1.2 m/s can be obtained for a signal level of 1000 detected photons which corresponds to signal levels in the boundary layer. For the molecular based measurement, we describe the use of a crossover region where the sensitivity of a molecular and aerosol-based measurement are equal. This desensitizes the molecular measurement to the effects of aerosol scattering and greatly simplifies the measurement. Simulations using a conical scanning spaceborne lidar at 355 nm give an accuracy of 2-3 m/s for altitudes of 2-15 km for a 1 km vertical resolution, a satellite altitude of 400 km, and a 200 km x 200 km spatial.

Understanding the detection of carbon in austenitic high-Mn steel using atom probe tomography.

PubMed

Marceau, R K W; Choi, P; Raabe, D

2013-09-01

A high-Mn TWIP steel having composition Fe-22Mn-0.6C (wt%) is considered in this study, where the need for accurate and quantitative analysis of clustering and short-range ordering by atom probe analysis requires a better understanding of the detection of carbon in this system. Experimental measurements reveal that a high percentage of carbon atoms are detected as molecular ion species and on multiple hit events, which is discussed with respect to issues such as optimal experimental parameters, correlated field evaporation and directional walk/migration of carbon atoms at the surface of the specimen tip during analysis. These phenomena impact the compositional and spatial accuracy of the atom probe measurement and thus require careful consideration for further cluster-finding analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

Object-Oriented Query Language For Events Detection From Images Sequences

NASA Astrophysics Data System (ADS)

Ganea, Ion Eugen

2015-09-01

In this paper is presented a method to represent the events extracted from images sequences and the query language used for events detection. Using an object oriented model the spatial and temporal relationships between salient objects and also between events are stored and queried. This works aims to unify the storing and querying phases for video events processing. The object oriented language syntax used for events processing allow the instantiation of the indexes classes in order to improve the accuracy of the query results. The experiments were performed on images sequences provided from sport domain and it shows the reliability and the robustness of the proposed language. To extend the language will be added a specific syntax for constructing the templates for abnormal events and for detection of the incidents as the final goal of the research.

Technical Note: Range verification system using edge detection method for a scintillator and a CCD camera system.

PubMed

Saotome, Naoya; Furukawa, Takuji; Hara, Yousuke; Mizushima, Kota; Tansho, Ryohei; Saraya, Yuichi; Shirai, Toshiyuki; Noda, Koji

2016-04-01

Three-dimensional irradiation with a scanned carbon-ion beam has been performed from 2011 at the authors' facility. The authors have developed the rotating-gantry equipped with the scanning irradiation system. The number of combinations of beam properties to measure for the commissioning is more than 7200, i.e., 201 energy steps, 3 intensities, and 12 gantry angles. To compress the commissioning time, quick and simple range verification system is required. In this work, the authors develop a quick range verification system using scintillator and charge-coupled device (CCD) camera and estimate the accuracy of the range verification. A cylindrical plastic scintillator block and a CCD camera were installed on the black box. The optical spatial resolution of the system is 0.2 mm/pixel. The camera control system was connected and communicates with the measurement system that is part of the scanning system. The range was determined by image processing. Reference range for each energy beam was determined by a difference of Gaussian (DOG) method and the 80% of distal dose of the depth-dose distribution that were measured by a large parallel-plate ionization chamber. The authors compared a threshold method and a DOG method. The authors found that the edge detection method (i.e., the DOG method) is best for the range detection. The accuracy of range detection using this system is within 0.2 mm, and the reproducibility of the same energy measurement is within 0.1 mm without setup error. The results of this study demonstrate that the authors' range check system is capable of quick and easy range verification with sufficient accuracy.

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery

USGS Publications Warehouse

Meng, Ran; Dennison, Philip E.; Jamison, Levi R.; van Riper, Charles; Nager, Pamela; Hultine, Kevin R.; Bean, Dan W.; Dudley, Tom

2012-01-01

The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States.

Technical Note: Range verification system using edge detection method for a scintillator and a CCD camera system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saotome, Naoya, E-mail: naosao@nirs.go.jp; Furukawa, Takuji; Hara, Yousuke

Purpose: Three-dimensional irradiation with a scanned carbon-ion beam has been performed from 2011 at the authors’ facility. The authors have developed the rotating-gantry equipped with the scanning irradiation system. The number of combinations of beam properties to measure for the commissioning is more than 7200, i.e., 201 energy steps, 3 intensities, and 12 gantry angles. To compress the commissioning time, quick and simple range verification system is required. In this work, the authors develop a quick range verification system using scintillator and charge-coupled device (CCD) camera and estimate the accuracy of the range verification. Methods: A cylindrical plastic scintillator blockmore » and a CCD camera were installed on the black box. The optical spatial resolution of the system is 0.2 mm/pixel. The camera control system was connected and communicates with the measurement system that is part of the scanning system. The range was determined by image processing. Reference range for each energy beam was determined by a difference of Gaussian (DOG) method and the 80% of distal dose of the depth-dose distribution that were measured by a large parallel-plate ionization chamber. The authors compared a threshold method and a DOG method. Results: The authors found that the edge detection method (i.e., the DOG method) is best for the range detection. The accuracy of range detection using this system is within 0.2 mm, and the reproducibility of the same energy measurement is within 0.1 mm without setup error. Conclusions: The results of this study demonstrate that the authors’ range check system is capable of quick and easy range verification with sufficient accuracy.« less

Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

PubMed Central

Dhingra, Madhur S; Artois, Jean; Robinson, Timothy P; Linard, Catherine; Chaiban, Celia; Xenarios, Ioannis; Engler, Robin; Liechti, Robin; Kuznetsov, Dmitri; Xiao, Xiangming; Dobschuetz, Sophie Von; Claes, Filip; Newman, Scott H; Dauphin, Gwenaëlle; Gilbert, Marius

2016-01-01

Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors. DOI: http://dx.doi.org/10.7554/eLife.19571.001 PMID:27885988

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Local indicators of geocoding accuracy (LIGA): theory and application

PubMed Central

Jacquez, Geoffrey M; Rommel, Robert

2009-01-01

Background Although sources of positional error in geographic locations (e.g. geocoding error) used for describing and modeling spatial patterns are widely acknowledged, research on how such error impacts the statistical results has been limited. In this paper we explore techniques for quantifying the perturbability of spatial weights to different specifications of positional error. Results We find that a family of curves describes the relationship between perturbability and positional error, and use these curves to evaluate sensitivity of alternative spatial weight specifications to positional error both globally (when all locations are considered simultaneously) and locally (to identify those locations that would benefit most from increased geocoding accuracy). We evaluate the approach in simulation studies, and demonstrate it using a case-control study of bladder cancer in south-eastern Michigan. Conclusion Three results are significant. First, the shape of the probability distributions of positional error (e.g. circular, elliptical, cross) has little impact on the perturbability of spatial weights, which instead depends on the mean positional error. Second, our methodology allows researchers to evaluate the sensitivity of spatial statistics to positional accuracy for specific geographies. This has substantial practical implications since it makes possible routine sensitivity analysis of spatial statistics to positional error arising in geocoded street addresses, global positioning systems, LIDAR and other geographic data. Third, those locations with high perturbability (most sensitive to positional error) and high leverage (that contribute the most to the spatial weight being considered) will benefit the most from increased positional accuracy. These are rapidly identified using a new visualization tool we call the LIGA scatterplot. Herein lies a paradox for spatial analysis: For a given level of positional error increasing sample density to more accurately follow the underlying population distribution increases perturbability and introduces error into the spatial weights matrix. In some studies positional error may not impact the statistical results, and in others it might invalidate the results. We therefore must understand the relationships between positional accuracy and the perturbability of the spatial weights in order to have confidence in a study's results. PMID:19863795

High-resolution high-sensitivity and truly distributed optical frequency domain reflectometry for structural crack detection

NASA Astrophysics Data System (ADS)

Li, Wenhai; Bao, Xiaoyi; Chen, Liang

2014-05-01

Optical Frequency Domain Reflectometry (OFDR) with the use of polarization maintaining fiber (PMF) is capable of distinguishing strain and temperature, which is critical for successful field applications such as structural health monitoring (SHM) and smart material. Location-dependent measurement sensitivities along PMF are compensated by cross- and auto-correlations measurements of the spectra form a distributed parameter matrix. Simultaneous temperature and strain measurement accuracy of 1μstrain and 0.1°C is achieved with 2.5mm spatial resolution in over 180m range.

Composite x-ray pinholes for time-resolved microphotography of laser compressed targets.

PubMed

Attwood, D T; Weinstein, B W; Wuerker, R F

1977-05-01

Composite x-ray pinholes having dichroic properties are presented. These pinholes permit both x-ray imaging and visible alignment with micron accuracy by presenting different apparent apertures in these widely disparate regions of the spectrum. Their use is mandatory in certain applications in which the x-ray detection consists of a limited number of resolvable elements whose use one wishes to maximize. Mating the pinhole camera with an x-ray streaking camera is described, along with experiments which spatially and temporally resolve the implosion of laser irradiated targets.

Few-cycle carrier envelope phase-dependent stereo detection of electrons.

PubMed

Verhoef, Aart J; Fernández, Alma; Lezius, Matthias; O'Keeffe, Kevin; Uiberacker, Matthias; Krausz, Ferenc

2006-12-01

The spatial distribution of electrons emitted from atoms by few-cycle optical fields is known to be dependent on the carrier envelope phase, i.e., the phase of the field with respect to the pulse envelope. With respect to Paulus et al. [Phys. Rev. Lett.91, 253004 (2003)] we propose a greatly simplified device to measure and control the carrier envelope phase of few-cycle pulses with an accuracy of better than pi/10 based on this principle. We compared different schemes to control the carrier envelope phase of our pulses.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011.

PubMed

Shields, Timothy; Pinchoff, Jessie; Lubinda, Jailos; Hamapumbu, Harry; Searle, Kelly; Kobayashi, Tamaki; Thuma, Philip E; Moss, William J; Curriero, Frank C

2016-05-31

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Selective 4D modelling framework for spatial-temporal land information management system

NASA Astrophysics Data System (ADS)

Doulamis, Anastasios; Soile, Sofia; Doulamis, Nikolaos; Chrisouli, Christina; Grammalidis, Nikos; Dimitropoulos, Kosmas; Manesis, Charalambos; Potsiou, Chryssy; Ioannidis, Charalabos

2015-06-01

This paper introduces a predictive (selective) 4D modelling framework where only the spatial 3D differences are modelled at the forthcoming time instances, while regions of no significant spatial-temporal alterations remain intact. To accomplish this, initially spatial-temporal analysis is applied between 3D digital models captured at different time instances. So, the creation of dynamic change history maps is made. Change history maps indicate spatial probabilities of regions needed further 3D modelling at forthcoming instances. Thus, change history maps are good examples for a predictive assessment, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 4D Land Information Management System (LIMS) is implemented using open interoperable standards based on the CityGML framework. CityGML allows the description of the semantic metadata information and the rights of the land resources. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 4D LIMS digital parcels and the respective semantic information. The open source 3DCityDB incorporating a PostgreSQL geo-database is used to manage and manipulate 3D data and their semantics. An application is made to detect the change through time of a 3D block of plots in an urban area of Athens, Greece. Starting with an accurate 3D model of the buildings in 1983, a change history map is created using automated dense image matching on aerial photos of 2010. For both time instances meshes are created and through their comparison the changes are detected.

Qualitative and quantitative three-dimensional accuracy of a single tooth captured by elastomeric impression materials: an in vitro study.

PubMed

Schaefer, Oliver; Schmidt, Monika; Goebel, Roland; Kuepper, Harald

2012-09-01

The accuracy of impressions has been described in 1 or 2 dimensions, whereas it is most desirable to evaluate the accuracy of impressions spatially, in 3 dimensions. The purpose of this study was to demonstrate the accuracy and reproducibility of a 3-dimensional (3-D) approach to assessing impression preciseness and to quantitatively comparing the occlusal correctness of gypsum dies made with different impression materials. By using an aluminum replica of a maxillary molar, single-step dual viscosity impressions were made with 1 polyether/vinyl polysiloxane hybrid material (Identium), 1 vinyl polysiloxane (Panasil), and 1 polyether (Impregum) (n=5). Corresponding dies were made of Type IV gypsum and were optically digitized and aligned to the virtual reference of the aluminum tooth. Accuracy was analyzed by computing mean quadratic deviations between the virtual reference and the gypsum dies, while deviations of the dies among one another determined the reproducibility of the method. The virtual reference was adapted to create 15 occlusal contact points. The percentage of contact points deviating within a ±10 µm tolerance limit (PDP(10) = Percentage of Deviating Points within ±10 µm Tolerance) was set as the index for assessing occlusal accuracy. Visual results for the difference from the reference tooth were displayed with colors, whereas mean deviation values as well as mean PDP(10) differences were analyzed with a 1-way ANOVA and Scheffé post hoc comparisons (α=.05). Objective characterization of accuracy showed smooth axial surfaces to be undersized, whereas occlusal surfaces were accurate or enlarged when compared to the original tooth. The accuracy of the gypsum replicas ranged between 3 and 6 µm, while reproducibility results varied from 2 to 4 µm. Mean (SD) PDP(10)-values were: Panasil 91% (±11), Identium 77% (±4) and Impregum 29% (±3). One-way ANOVA detected significant differences among the subjected impression materials (P<.001). The accuracy and reproducibility of impressions were determined by 3-D analysis. Results were presented as color images and the newly developed PDP(10)-index was successfully used to quantify spatial dimensions for complex occlusal anatomy. Impression materials with high PDP(10)-values were shown to reproduce occlusal dimensions the most accurately. Copyright © 2012 The Editorial Council of the Journal of Prosthetic Dentistry. Published by Mosby, Inc. All rights reserved.

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

Respiration-rate estimation of a moving target using impulse-based ultra wideband radars.

PubMed

Sharafi, Azadeh; Baboli, Mehran; Eshghi, Mohammad; Ahmadian, Alireza

2012-03-01

Recently, Ultra-wide band signals have become attractive for their particular advantage of having high spatial resolution and good penetration ability which makes them suitable in medical applications. One of these applications is wireless detection of heart rate and respiration rate. Two hypothesis of static environment and fixed patient are considered in the method presented in previous literatures which are not valid for long term monitoring of ambulant patients. In this article, a new method to detect the respiration rate of a moving target is presented. The first algorithm is applied to the simulated and experimental data for detecting respiration rate of a fixed target. Then, the second algorithm is developed to detect respiration rate of a moving target. The proposed algorithm uses correlation for body movement cancellation, and then detects the respiration rate based on energy in frequency domain. The results of algorithm prove an accuracy of 98.4 and 97% in simulated and experimental data, respectively.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Entropy of space-time outcome in a movement speed-accuracy task.

PubMed

Hsieh, Tsung-Yu; Pacheco, Matheus Maia; Newell, Karl M

2015-12-01

The experiment reported was set-up to investigate the space-time entropy of movement outcome as a function of a range of spatial (10, 20 and 30 cm) and temporal (250-2500 ms) criteria in a discrete aiming task. The variability and information entropy of the movement spatial and temporal errors considered separately increased and decreased on the respective dimension as a function of an increment of movement velocity. However, the joint space-time entropy was lowest when the relative contribution of spatial and temporal task criteria was comparable (i.e., mid-range of space-time constraints), and it increased with a greater trade-off between spatial or temporal task demands, revealing a U-shaped function across space-time task criteria. The traditional speed-accuracy functions of spatial error and temporal error considered independently mapped to this joint space-time U-shaped entropy function. The trade-off in movement tasks with joint space-time criteria is between spatial error and timing error, rather than movement speed and accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

The effect of spatial, spectral and radiometric factors on classification accuracy using thematic mapper data

NASA Technical Reports Server (NTRS)

Wrigley, R. C.; Acevedo, W.; Alexander, D.; Buis, J.; Card, D.

1984-01-01

An experiment of a factorial design was conducted to test the effects on classification accuracy of land cover types due to the improved spatial, spectral and radiometric characteristics of the Thematic Mapper (TM) in comparison to the Multispectral Scanner (MSS). High altitude aircraft scanner data from the Airborne Thematic Mapper instrument was acquired over central California in August, 1983 and used to simulate Thematic Mapper data as well as all combinations of the three characteristics for eight data sets in all. Results for the training sites (field center pixels) showed better classification accuracies for MSS spatial resolution, TM spectral bands and TM radiometry in order of importance.

Development of a Liquefied Noble Gas Time Projection Chamber

NASA Astrophysics Data System (ADS)

Lesser, Ezra; White, Aaron; Aidala, Christine

2015-10-01

Liquefied noble gas detectors have been used for various applications in recent years for detecting neutrinos, neutrons, photons, and potentially dark matter. The University of Michigan is developing a detector with liquid argon to produce scintillation light and ionization electrons. Our data collection method will allow high-resolution energy measurement and spatial reconstruction of detected particles by using multi-pixel silicon photomultipliers (SiPM) and a cylindrical time projection chamber (TPC) with a multi-wire endplate. We have already designed a liquid argon condenser and purification unit surrounded by an insulating vacuum, constructed circuitry for temperature and pressure sensors, and created software to obtain high-accuracy sensor readouts. The status of detector development will be presented. Funded through the Michigan Memorial Phoenix Project.

Healthy versus Entorhinal Cortical Atrophy Identification in Asymptomatic APOE4 Carriers at Risk for Alzheimer’s Disease

PubMed Central

Konishi, Kyoko; Joober, Ridha; Poirier, Judes; MacDonald, Kathleen; Chakravarty, Mallar; Patel, Raihaan; Breitner, John; Bohbot, Véronique D.

2018-01-01

Early detection of Alzheimer’s disease (AD) has been challenging as current biomarkers are invasive and costly. Strong predictors of future AD diagnosis include lower volume of the hippocampus and entorhinal cortex, as well as the ɛ4 allele of the Apolipoprotein E gene (APOE) gene. Therefore, studying functions that are critically mediated by the hippocampus and entorhinal cortex, such as spatial memory, in APOE ɛ4 allele carriers, may be key to the identification of individuals at risk of AD, prior to the manifestation of cognitive impairments. Using a virtual navigation task developed in-house, specifically designed to assess spatial versus non-spatial strategies, the current study is the first to differentiate functional and structural differences within APOE ɛ4 allele carriers. APOE ɛ4 allele carriers that predominantly use non-spatial strategies have decreased fMRI activity in the hippocampus and increased atrophy in the hippocampus, entorhinal cortex, and fimbria compared to APOE ɛ4 allele carriers who use spatial strategies. In contrast, APOE ɛ4 allele carriers who use spatial strategies have grey matter levels comparable to non-APOE ɛ4 allele carriers. Furthermore, in a leave-one-out analysis, grey matter in the entorhinal cortex could predict navigational strategy with 92% accuracy. PMID:29278888

Healthy versus Entorhinal Cortical Atrophy Identification in Asymptomatic APOE4 Carriers at Risk for Alzheimer's Disease.

PubMed

Konishi, Kyoko; Joober, Ridha; Poirier, Judes; MacDonald, Kathleen; Chakravarty, Mallar; Patel, Raihaan; Breitner, John; Bohbot, Véronique D

2018-01-01

Early detection of Alzheimer's disease (AD) has been challenging as current biomarkers are invasive and costly. Strong predictors of future AD diagnosis include lower volume of the hippocampus and entorhinal cortex, as well as the ɛ4 allele of the Apolipoprotein E gene (APOE) gene. Therefore, studying functions that are critically mediated by the hippocampus and entorhinal cortex, such as spatial memory, in APOE ɛ4 allele carriers, may be key to the identification of individuals at risk of AD, prior to the manifestation of cognitive impairments. Using a virtual navigation task developed in-house, specifically designed to assess spatial versus non-spatial strategies, the current study is the first to differentiate functional and structural differences within APOE ɛ4 allele carriers. APOE ɛ4 allele carriers that predominantly use non-spatial strategies have decreased fMRI activity in the hippocampus and increased atrophy in the hippocampus, entorhinal cortex, and fimbria compared to APOE ɛ4 allele carriers who use spatial strategies. In contrast, APOE ɛ4 allele carriers who use spatial strategies have grey matter levels comparable to non-APOE ɛ4 allele carriers. Furthermore, in a leave-one-out analysis, grey matter in the entorhinal cortex could predict navigational strategy with 92% accuracy.

Spatial adaption procedures on unstructured meshes for accurate unsteady aerodynamic flow computation

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Batina, John T.; Yang, Henry T. Y.

1991-01-01

Spatial adaption procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaption procedures were developed and implemented within a two-dimensional unstructured-grid upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in a high gradient region or the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational costs. A detailed description is given of the enrichment and coarsening procedures and comparisons with alternative results and experimental data are presented to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady transonic results, obtained using spatial adaption for the NACA 0012 airfoil, are shown to be of high spatial accuracy, primarily in that the shock waves are very sharply captured. The results were obtained with a computational savings of a factor of approximately fifty-three for a steady case and as much as twenty-five for the unsteady cases.

Spatial adaption procedures on unstructured meshes for accurate unsteady aerodynamic flow computation

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Yang, Henry T. Y.; Batina, John T.

1991-01-01

Spatial adaption procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaption procedures were developed and implemented within a two-dimensional unstructured-grid upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in high gradient regions of the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational cost. The paper gives a detailed description of the enrichment and coarsening procedures and presents comparisons with alternative results and experimental data to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady transonic results, obtained using spatial adaption for the NACA 0012 airfoil, are shown to be of high spatial accuracy, primarily in that the shock waves are very sharply captured. The results were obtained with a computational savings of a factor of approximately fifty-three for a steady case and as much as twenty-five for the unsteady cases.

Initial Performance of the Aspect System on the Chandra Observatory: Post-Facto Aspect Reconstruction

NASA Technical Reports Server (NTRS)

Aldcroft, T.; Karovska, M.; Cresitello-Dittmar, M.; Cameron, R.

2000-01-01

The aspect system of the Chandra Observatory plays a key role in realizing the full potential of Chandra's x-ray optics and detectors. To achieve the highest spatial and spectral resolution (for grating observations), an accurate post-facto time history of the spacecraft attitude and internal alignment is needed. The CXC has developed a suite of tools which process sensor data from the aspect camera assembly and gyroscopes, and produce the spacecraft aspect solution. In this poster, the design of the aspect pipeline software is briefly described, followed by details of aspect system performance during the first eight months of flight. The two key metrics of aspect performance are: image reconstruction accuracy, which measures the x-ray image blurring introduced by aspect; and celestial location, which is the accuracy of detected source positions in absolute sky coordinates.

An Algorithm of Association Rule Mining for Microbial Energy Prospection

PubMed Central

Shaheen, Muhammad; Shahbaz, Muhammad

2017-01-01

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are then correlated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules. PMID:28393846

A familiar pattern? Semantic memory contributes to the enhancement of visuo-spatial memories.

PubMed

Riby, Leigh M; Orme, Elizabeth

2013-03-01

In this study we quantify for the first time electrophysiological components associated with incorporating long-term semantic knowledge with visuo-spatial information using two variants of a traditional matrix patterns task. Results indicated that the matrix task with greater semantic content was associated with enhanced accuracy and RTs in a change-detection paradigm; this was also associated with increased P300 and N400 components as well as a sustained negative slow wave (NSW). In contrast, processing of the low semantic stimuli was associated with an increased N200 and a reduction in the P300. These findings suggest that semantic content can aid in reducing early visual processing of information and subsequent memory load by unitizing complex patterns into familiar forms. The N400/NSW may be associated with the requirements for maintaining visuo-spatial information about semantic forms such as orientation and relative location. Evidence for individual differences in semantic elaboration strategies used by participants is also discussed. Copyright © 2012 Elsevier Inc. All rights reserved.

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality (CCQ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality ( CCQ ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Cloud Statistics and Discrimination in the Polar Regions

NASA Astrophysics Data System (ADS)

Chan, M.; Comiso, J. C.

2012-12-01

Despite their important role in the climate system, cloud cover and their statistics are poorly known, especially in the polar regions, where clouds are difficult to discriminate from snow covered surfaces. The advent of the A-train, which included Aqua/MODIS, CALIPSO/CALIOP and CloudSat/CPR sensors has provided an opportunity to improve our ability to accurately characterize the cloud cover. MODIS provides global coverage at a relatively good temporal and spatial resolution while CALIOP and CPR provide limited nadir sampling but accurate characterization of the vertical structure and phase of the cloud cover. Over the polar regions, cloud detection from a passive sensors like MODIS is challenging because of the presence of cold and highly reflective surfaces such as snow, sea-ice, glaciers, and ice-sheet, which have surface signatures similar to those of clouds. On the other hand, active sensors such as CALIOP and CPR are not only very sensitive to the presence of clouds but can also provide information about its microphysical characteristics. However, these nadir-looking sensors have sparse spatial coverage and their global data can have data spatial gaps of up to 100 km. We developed a polar cloud detection system for MODIS that is trained using collocated data from CALIOP and CPR. In particular, we employ a machine learning system that reads the radiative profile observed by MODIS and determine whether the field of view is cloudy or clear. Results have shown that the improved cloud detection scheme performs better than typical cloud mask algorithms using a validation data set not used for training. A one-year data set was generated and results indicate that daytime cloud detection accuracies improved from 80.1% to 92.6% (over sea-ice) and 71.2% to 87.4% (over ice-sheet) with CALIOP data used as the baseline. Significant improvements are also observed during nighttime, where cloud detection accuracies increase by 19.8% (over sea-ice) and 11.6% (over ice-sheet). The immediate impact of the new algorithm is that it can minimize large biases of MODIS-derived cloud amount over the Polar Regions and thus a more realistic and high quality global cloud statistics. In particular, our results show that cloud fraction in the Arctic is typically 81.2 % during daytime and 84.0% during nighttime. This is significantly higher than the 71.8% and 58.5%, respectively, derived from standard MODIS cloud product.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

Segmentation and detection of fluorescent 3D spots.

PubMed

Ram, Sundaresh; Rodríguez, Jeffrey J; Bosco, Giovanni

2012-03-01

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques. Copyright © 2012 International Society for Advancement of Cytometry.

Vacuum compatible sample positioning device for matrix assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aizikov, Konstantin; Lin, Tzu-Yung; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215

The high mass accuracy and resolving power of Fourier transform ion cyclotron resonance mass spectrometers (FT-ICR MS) make them ideal mass detectors for mass spectrometry imaging (MSI), promising to provide unmatched molecular resolution capabilities. The intrinsic low tolerance of FT-ICR MS to RF interference, however, along with typically vertical positioning of the sample, and MSI acquisition speed requirements present numerous engineering challenges in creating robotics capable of achieving the spatial resolution to match. This work discusses a two-dimensional positioning stage designed to address these issues. The stage is capable of operating in {approx}1 x 10{sup -8} mbar vacuum. The rangemore » of motion is set to 100 mm x 100 mm to accommodate large samples, while the positioning accuracy is demonstrated to be less than 0.4 micron in both directions under vertical load over the entire range. This device was integrated into three different matrix assisted laser desorption/ionization (MALDI) FT-ICR instruments and showed no detectable RF noise. The ''oversampling'' MALDI-MSI experiments, under which the sample is completely ablated at each position, followed by the target movement of the distance smaller than the laser beam, conducted on the custom-built 7T FT-ICR MS demonstrate the stability and positional accuracy of the stage robotics which delivers high spatial resolution mass spectral images at a fraction of the laser spot diameter.« less

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

PubMed Central

Dijksterhuis, Chris; de Waard, Dick; Brookhuis, Karel A.; Mulder, Ben L. J. M.; de Jong, Ritske

2013-01-01

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications. PMID:23970851

Evaluation of Pan-Sharpening Methods for Automatic Shadow Detection in High Resolution Images of Urban Areas

NASA Astrophysics Data System (ADS)

de Azevedo, Samara C.; Singh, Ramesh P.; da Silva, Erivaldo A.

2017-04-01

Finer spatial resolution of areas with tall objects within urban environment causes intense shadows that lead to wrong information in urban mapping. Due to the shadows, automatic detection of objects (such as buildings, trees, structures, towers) and to estimate the surface coverage from high spatial resolution is difficult. Thus, automatic shadow detection is the first necessary preprocessing step to improve the outcome of many remote sensing applications, particularly for high spatial resolution images. Efforts have been made to explore spatial and spectral information to evaluate such shadows. In this paper, we have used morphological attribute filtering to extract contextual relations in an efficient multilevel approach for high resolution images. The attribute selected for the filtering was the area estimated from shadow spectral feature using the Normalized Saturation-Value Difference Index (NSVDI) derived from pan-sharpening images. In order to assess the quality of fusion products and the influence on shadow detection algorithm, we evaluated three pan-sharpening methods - Intensity-Hue-Saturation (IHS), Principal Components (PC) and Gran-Schmidt (GS) through the image quality measures: Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Dimensionless Global Error in Synthesis (ERGAS) and Universal Image Quality Index (UIQI). Experimental results over Worldview II scene from São Paulo city (Brazil) show that GS method provides good correlation with original multispectral bands with no radiometric and contrast distortion. The automatic method using GS method for NSDVI generation clearly provide a clear distinction of shadows and non-shadows pixels with an overall accuracy more than 90%. The experimental results confirm the effectiveness of the proposed approach which could be used for further shadow removal and reliable for object recognition, land-cover mapping, 3D reconstruction, etc. especially in developing countries where land use and land cover are rapidly changing with tall objects within urban areas.

Examining Impulse-Variability in Kicking.

PubMed

Chappell, Andrew; Molina, Sergio L; McKibben, Jonathon; Stodden, David F

2016-07-01

This study examined variability in kicking speed and spatial accuracy to test the impulse-variability theory prediction of an inverted-U function and the speed-accuracy trade-off. Twenty-eight 18- to 25-year-old adults kicked a playground ball at various percentages (50-100%) of their maximum speed at a wall target. Speed variability and spatial error were analyzed using repeated-measures ANOVA with built-in polynomial contrasts. Results indicated a significant inverse linear trajectory for speed variability (p < .001, η2= .345) where 50% and 60% maximum speed had significantly higher variability than the 100% condition. A significant quadratic fit was found for spatial error scores of mean radial error (p < .0001, η2 = .474) and subject-centroid radial error (p < .0001, η2 = .453). Findings suggest variability and accuracy of multijoint, ballistic skill performance may not follow the general principles of impulse-variability theory or the speed-accuracy trade-off.

Tomographic imaging of OH laser-induced fluorescence in laminar and turbulent jet flames

NASA Astrophysics Data System (ADS)

Li, Tao; Pareja, Jhon; Fuest, Frederik; Schütte, Manuel; Zhou, Yihui; Dreizler, Andreas; Böhm, Benjamin

2018-01-01

In this paper a new approach for 3D flame structure diagnostics using tomographic laser-induced fluorescence (Tomo-LIF) of the OH radical was evaluated. The approach combined volumetric illumination with a multi-camera detection system of eight views. Single-shot measurements were performed in a methane/air premixed laminar flame and in a non-premixed turbulent methane jet flame. 3D OH fluorescence distributions in the flames were reconstructed using the simultaneous multiplicative algebraic reconstruction technique. The tomographic measurements were compared and validated against results of OH-PLIF in the laminar flame. The effects of the experimental setup of the detection system and the size of the volumetric illumination on the quality of the tomographic reconstructions were evaluated. Results revealed that the Tomo-LIF is suitable for volumetric reconstruction of flame structures with acceptable spatial resolution and uncertainty. It was found that the number of views and their angular orientation have a strong influence on the quality and accuracy of the tomographic reconstruction while the illumination volume thickness influences mainly the spatial resolution.

Development of full-field optical spatial coherence tomography system for automated identification of malaria using the multilevel ensemble classifier.

PubMed

Singla, Neeru; Srivastava, Vishal; Mehta, Dalip Singh

2018-05-01

Malaria is a life-threatening infectious blood disease affecting humans and other animals caused by parasitic protozoans belonging to the Plasmodium type especially in developing countries. The gold standard method for the detection of malaria is through the microscopic method of chemically treated blood smears. We developed an automated optical spatial coherence tomographic system using a machine learning approach for a fast identification of malaria cells. In this study, 28 samples (15 healthy, 13 malaria infected stages of red blood cells) were imaged by the developed system and 13 features were extracted. We designed a multilevel ensemble-based classifier for the quantitative prediction of different stages of the malaria cells. The proposed classifier was used by repeating k-fold cross validation dataset and achieve a high-average accuracy of 97.9% for identifying malaria infected late trophozoite stage of cells. Overall, our proposed system and multilevel ensemble model has a substantial quantifiable potential to detect the different stages of malaria infection without staining or expert. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cognitive ability is heritable and predicts the success of an alternative mating tactic

PubMed Central

Smith, Carl; Philips, André; Reichard, Martin

2015-01-01

The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits—the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. PMID:26041347

Cognitive ability is heritable and predicts the success of an alternative mating tactic.

PubMed

Smith, Carl; Philips, André; Reichard, Martin

2015-06-22

The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits--the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A test of the reward-value hypothesis.

PubMed

Smith, Alexandra E; Dalecki, Stefan J; Crystal, Jonathon D

2017-03-01

Rats retain source memory (memory for the origin of information) over a retention interval of at least 1 week, whereas their spatial working memory (radial maze locations) decays within approximately 1 day. We have argued that different forgetting functions dissociate memory systems. However, the two tasks, in our previous work, used different reward values. The source memory task used multiple pellets of a preferred food flavor (chocolate), whereas the spatial working memory task provided access to a single pellet of standard chow-flavored food at each location. Thus, according to the reward-value hypothesis, enhanced performance in the source memory task stems from enhanced encoding/memory of a preferred reward. We tested the reward-value hypothesis by using a standard 8-arm radial maze task to compare spatial working memory accuracy of rats rewarded with either multiple chocolate or chow pellets at each location using a between-subjects design. The reward-value hypothesis predicts superior accuracy for high-valued rewards. We documented equivalent spatial memory accuracy for high- and low-value rewards. Importantly, a 24-h retention interval produced equivalent spatial working memory accuracy for both flavors. These data are inconsistent with the reward-value hypothesis and suggest that reward value does not explain our earlier findings that source memory survives unusually long retention intervals.

Coarse climate change projections for species living in a fine-scaled world.

PubMed

Nadeau, Christopher P; Urban, Mark C; Bridle, Jon R

2017-01-01

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change. © 2016 John Wiley & Sons Ltd.

Automatic detection of a hand-held needle in ultrasound via phased-based analysis of the tremor motion

NASA Astrophysics Data System (ADS)

Beigi, Parmida; Salcudean, Septimiu E.; Rohling, Robert; Ng, Gary C.

2016-03-01

This paper presents an automatic localization method for a standard hand-held needle in ultrasound based on temporal motion analysis of spatially decomposed data. Subtle displacement arising from tremor motion has a periodic pattern which is usually imperceptible in the intensity image but may convey information in the phase image. Our method aims to detect such periodic motion of a hand-held needle and distinguish it from intrinsic tissue motion, using a technique inspired by video magnification. Complex steerable pyramids allow specific design of the wavelets' orientations according to the insertion angle as well as the measurement of the local phase. We therefore use steerable pairs of even and odd Gabor wavelets to decompose the ultrasound B-mode sequence into various spatial frequency bands. Variations of the local phase measurements in the spatially decomposed input data is then temporally analyzed using a finite impulse response bandpass filter to detect regions with a tremor motion pattern. Results obtained from different pyramid levels are then combined and thresholded to generate the binary mask input for the Hough transform, which determines an estimate of the direction angle and discards some of the outliers. Polynomial fitting is used at the final stage to remove any remaining outliers and improve the trajectory detection. The detected needle is finally added back to the input sequence as an overlay of a cloud of points. We demonstrate the efficiency of our approach to detect the needle using subtle tremor motion in an agar phantom and in-vivo porcine cases where intrinsic motion is also present. The localization accuracy was calculated by comparing to expert manual segmentation, and presented in (mean, standard deviation and root-mean-square error) of (0.93°, 1.26° and 0.87°) and (1.53 mm, 1.02 mm and 1.82 mm) for the trajectory and the tip, respectively.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

Sensitivity, accuracy, and precision issues in opto-electronic holography based on fiber optics and high-spatial- and high-digitial-resolution cameras

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Yokum, Jeffrey S.; Pryputniewicz, Ryszard J.

2002-06-01

Sensitivity, accuracy, and precision characteristics in quantitative optical metrology techniques, and specifically in optoelectronic holography based on fiber optics and high-spatial and high-digital resolution cameras, are discussed in this paper. It is shown that sensitivity, accuracy, and precision dependent on both, the effective determination of optical phase and the effective characterization of the illumination-observation conditions. Sensitivity, accuracy, and precision are investigated with the aid of National Institute of Standards and Technology (NIST) traceable gages, demonstrating the applicability of quantitative optical metrology techniques to satisfy constantly increasing needs for the study and development of emerging technologies.

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

USGS Publications Warehouse

Boykin, K.G.; Thompson, B.C.; Propeck-Gray, S.

2010-01-01

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

PubMed

Vanguelova, E I; Bonifacio, E; De Vos, B; Hoosbeek, M R; Berger, T W; Vesterdal, L; Armolaitis, K; Celi, L; Dinca, L; Kjønaas, O J; Pavlenda, P; Pumpanen, J; Püttsepp, Ü; Reidy, B; Simončič, P; Tobin, B; Zhiyanski, M

2016-11-01

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

The Review of Nuclear Microscopy Techniques: An Approach for Nondestructive Trace Elemental Analysis and Mapping of Biological Materials.

PubMed

Mulware, Stephen Juma

2015-01-01

The properties of many biological materials often depend on the spatial distribution and concentration of the trace elements present in a matrix. Scientists have over the years tried various techniques including classical physical and chemical analyzing techniques each with relative level of accuracy. However, with the development of spatially sensitive submicron beams, the nuclear microprobe techniques using focused proton beams for the elemental analysis of biological materials have yielded significant success. In this paper, the basic principles of the commonly used microprobe techniques of STIM, RBS, and PIXE for trace elemental analysis are discussed. The details for sample preparation, the detection, and data collection and analysis are discussed. Finally, an application of the techniques to analysis of corn roots for elemental distribution and concentration is presented.

Validation of spatially resolved all sky imager derived DNI nowcasts

NASA Astrophysics Data System (ADS)

Kuhn, Pascal; Wilbert, Stefan; Schüler, David; Prahl, Christoph; Haase, Thomas; Ramirez, Lourdes; Zarzalejo, Luis; Meyer, Angela; Vuilleumier, Laurent; Blanc, Philippe; Dubrana, Jean; Kazantzidis, Andreas; Schroedter-Homscheidt, Marion; Hirsch, Tobias; Pitz-Paal, Robert

2017-06-01

Mainly due to clouds, Direct Normal Irradiance (DNI) displays short-term local variabilities affecting the efficiency of concentrating solar power (CSP) plants. To enable efficient plant operation, DNI nowcasts in high spatial and temporal resolutions for 15 to 30 minutes ahead are required. Ground-based All Sky Imagers (ASI) can be used to detect, track and predict 3D positions of clouds possibly shading the plant. The accuracy and reliability of these ASI-derived DNI nowcasts must be known to allow its application in solar power plants. Within the framework of the European project DNICast, an ASI-based nowcasting system was developed and implemented at the Plataforma Solar de Almería (PSA). Its validation methodology and validation results are presented in this work. The nowcasting system outperforms persistence forecasts for volatile irradiance situations.

Pattern recognition of electronic bit-sequences using a semiconductor mode-locked laser and spatial light modulators

NASA Astrophysics Data System (ADS)

Bhooplapur, Sharad; Akbulut, Mehmetkan; Quinlan, Franklyn; Delfyett, Peter J.

2010-04-01

A novel scheme for recognition of electronic bit-sequences is demonstrated. Two electronic bit-sequences that are to be compared are each mapped to a unique code from a set of Walsh-Hadamard codes. The codes are then encoded in parallel on the spectral phase of the frequency comb lines from a frequency-stabilized mode-locked semiconductor laser. Phase encoding is achieved by using two independent spatial light modulators based on liquid crystal arrays. Encoded pulses are compared using interferometric pulse detection and differential balanced photodetection. Orthogonal codes eight bits long are compared, and matched codes are successfully distinguished from mismatched codes with very low error rates, of around 10-18. This technique has potential for high-speed, high accuracy recognition of bit-sequences, with applications in keyword searches and internet protocol packet routing.

Comparison of the KSC-ER Cloud-to-Ground Lightning Surveillance System (CGLSS) and the U.S. National Lightning Detection Network(TradeMark)(NLDN)

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Cummins, Kenneth L.; Krider, E. Philip

2007-01-01

The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground lightning detection networks, CGLSS and NLDN, during ground and launch operations at the KSC-ER. For these applications, it is very important to understand the location accuracy and detection efficiency of each network near the KSC-ER. If a cloud-to-ground (CG) lightning strike is missed or mis-located by even a small amount, the result could have significant safety implications, require expensive retests, or create unnecessary delays or scrubs in launches. Therefore, it is important to understand the performance of each lightning detection system in considerable detail. To evaluate recent upgrades in the CGLSS sensors in 2000 and the entire NLDN in 2002- 2003, we have compared. measurements provided by these independent networks in the summers of 2005 and 2006. Our analyses have focused on the fraction of first strokes reported individually and in-common by each network (flash detection efficiency), the spatial separation between the strike points reported by both networks (relative location accuracy), and the values of the estimated peak current, Ip, reported by each network. The results within 100 km of the KSC-ER show that the networks produce very similar values of Ip (except for a small scaling difference) and that the relative location accuracy is consistent with model estimates that give median values of 200-300m for the CGLSS and 600-700m for the NLDN in the region of the KSC-ER. Because of differences in the network geometries and sensor gains, the NLDN does not report 10-20% of the flashes that have a low Ip (2 kA < |Ip| < 16 kA), both networks report 99 % of the flashes that have intermediate values of Ip (16< |Ip| < 50 kA), and the CGLSS fails to report 20-30% of the high-current events (|Ip| >=0 kA).

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

NASA Astrophysics Data System (ADS)

Xiong, L.; Wang, G.; Wessel, P.

2017-12-01

Terrestrial laser scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), has been frequently applied to build bare-earth digital elevation models (DEMs) for high-accuracy geomorphology studies. The point clouds acquired from TLS often achieve a spatial resolution at fingerprint (e.g., 3cm×3cm) to handprint (e.g., 10cm×10cm) level. A downsampling process has to be applied to decimate the massive point clouds and obtain portable DEMs. It is well known that downsampling can result in aliasing that causes different signal components to become indistinguishable when the signal is reconstructed from the datasets with a lower sampling rate. Conventional DEMs are mainly the results of upsampling of sparse elevation measurements from land surveying, satellite remote sensing, and aerial photography. As a consequence, the effects of aliasing have not been fully investigated in the open literature of DEMs. This study aims to investigate the spatial aliasing problem and implement an anti-aliasing procedure of regridding dense TLS data. The TLS data collected in the beach and dune area near Freeport, Texas in the summer of 2015 are used for this study. The core idea of the anti-aliasing procedure is to apply a low-pass spatial filter prior to conducting downsampling. This article describes the successful use of a fourth-order Butterworth low-pass spatial filter employed in the Generic Mapping Tools (GMT) software package as anti-aliasing filters. The filter can be applied as an isotropic filter with a single cutoff wavelength or as an anisotropic filter with different cutoff wavelengths in the X and Y directions. The cutoff wavelength for the isotropic filter is recommended to be three times the grid size of the target DEM.

Influence of resolution in irrigated area mapping and area estimation

USGS Publications Warehouse

Velpuri, N.M.; Thenkabail, P.S.; Gumma, M.K.; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L.

2009-01-01

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that "the finer the spatial resolution of the sensor used, greater was the irrigated area derived," since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent). ?? 2009 American Society for Photogrammetry and Remote Sensing.

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

NASA Astrophysics Data System (ADS)

Xiong, Lin.; Wang, Guoquan; Wessel, Paul

2017-03-01

Terrestrial laser scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), has been frequently applied to build bare-earth digital elevation models (DEMs) for high-accuracy geomorphology studies. The point clouds acquired from TLS often achieve a spatial resolution at fingerprint (e.g., 3 cm×3 cm) to handprint (e.g., 10 cm×10 cm) level. A downsampling process has to be applied to decimate the massive point clouds and obtain manageable DEMs. It is well known that downsampling can result in aliasing that causes different signal components to become indistinguishable when the signal is reconstructed from the datasets with a lower sampling rate. Conventional DEMs are mainly the results of upsampling of sparse elevation measurements from land surveying, satellite remote sensing, and aerial photography. As a consequence, the effects of aliasing caused by downsampling have not been fully investigated in the open literature of DEMs. This study aims to investigate the spatial aliasing problem of regridding dense TLS data. The TLS data collected from the beach and dune area near Freeport, Texas in the summer of 2015 are used for this study. The core idea of the anti-aliasing procedure is to apply a low-pass spatial filter prior to conducting downsampling. This article describes the successful use of a fourth-order Butterworth low-pass spatial filter employed in the Generic Mapping Tools (GMT) software package as an anti-aliasing filter. The filter can be applied as an isotropic filter with a single cutoff wavelength or as an anisotropic filter with two different cutoff wavelengths in the X and Y directions. The cutoff wavelength for the isotropic filter is recommended to be three times the grid size of the target DEM.

The feasibility of using a universal Random Forest model to map tree height across different locations and vegetation types

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Jin, S.; Gao, S.; Hu, T.; Liu, J.; Xue, B. L.

2017-12-01

Tree height is an important forest structure parameter for understanding forest ecosystem and improving the accuracy of global carbon stock quantification. Light detection and ranging (LiDAR) can provide accurate tree height measurements, but its use in large-scale tree height mapping is limited by the spatial availability. Random Forest (RF) has been one of the most commonly used algorithms for mapping large-scale tree height through the fusion of LiDAR and other remotely sensed datasets. However, how the variances in vegetation types, geolocations and spatial scales of different study sites influence the RF results is still a question that needs to be addressed. In this study, we selected 16 study sites across four vegetation types in United States (U.S.) fully covered by airborne LiDAR data, and the area of each site was 100 km2. The LiDAR-derived canopy height models (CHMs) were used as the ground truth to train the RF algorithm to predict canopy height from other remotely sensed variables, such as Landsat TM imagery, terrain information and climate surfaces. To address the abovementioned question, 22 models were run under different combinations of vegetation types, geolocations and spatial scales. The results show that the RF model trained at one specific location or vegetation type cannot be used to predict tree height in other locations or vegetation types. However, by training the RF model using samples from all locations and vegetation types, a universal model can be achieved for predicting canopy height across different locations and vegetation types. Moreover, the number of training samples and the targeted spatial resolution of the canopy height product have noticeable influence on the RF prediction accuracy.

The Influence of Spatial Resolutions on the Retrieval Accuracy of Sea Surface Wind Speed with Cross-polarized C-band SAR images

NASA Astrophysics Data System (ADS)

Zhang, K.; Han, B.; Mansaray, L. R.; Xu, X.; Guo, Q.; Jingfeng, H.

2017-12-01

Synthetic aperture radar (SAR) instruments on board satellites are valuable for high-resolution wind field mapping, especially for coastal studies. Since the launch of Sentinel-1A on April 3, 2014, followed by Sentinel-1B on April 25, 2016, large amount of C-band SAR data have been added to a growing accumulation of SAR datasets (ERS-1/2, RADARSAT-1/2, ENVISAT). These new developments are of great significance for a wide range of applications in coastal sea areas, especially for high spatial resolution wind resource assessment, in which the accuracy of retrieved wind fields is extremely crucial. Recently, it is reported that wind speeds can also be retrieved from C-band cross-polarized SAR images, which is an important complement to wind speed retrieval from co-polarization. However, there is no consensus on the optimal resolution for wind speed retrieval from cross-polarized SAR images. This paper presents a comparison strategy for investigating the influence of spatial resolutions on sea surface wind speed retrieval accuracy with cross-polarized SAR images. Firstly, for wind speeds retrieved from VV-polarized images, the optimal geophysical C-band model (CMOD) function was selected among four CMOD functions. Secondly, the most suitable C-band cross-polarized ocean (C-2PO) model was selected between two C-2POs for the VH-polarized image dataset. Then, the VH-wind speeds retrieved by the selected C-2PO were compared with the VV-polarized sea surface wind speeds retrieved using the optimal CMOD, which served as reference, at different spatial resolutions. Results show that the VH-polarized wind speed retrieval accuracy increases rapidly with the decrease in spatial resolutions from 100 m to 1000 m, with a drop in RMSE of 42%. However, the improvement in wind speed retrieval accuracy levels off with spatial resolutions decreasing from 1000 m to 5000 m. This demonstrates that the pixel spacing of 1 km may be the compromising choice for the tradeoff between the spatial resolution and wind speed retrieval accuracy with cross-polarized images obtained from RADASAT-2 fine quad polarization mode. Figs. 1 illustrate the variation of the following statistical parameters: Bias, Corr, R2, RMSE and STD as a function of spatial resolution.

Enclosure Transform for Interest Point Detection From Speckle Imagery.

PubMed

Yongjian Yu; Jue Wang

2017-03-01

We present a fast enclosure transform (ET) to localize complex objects of interest from speckle imagery. This approach explores the spatial confinement on regional features from a sparse image feature representation. Unrelated, broken ridge features surrounding an object are organized collaboratively, giving rise to the enclosureness of the object. Three enclosure likelihood measures are constructed, consisting of the enclosure force, potential energy, and encloser count. In the transform domain, the local maxima manifest the locations of objects of interest, for which only the intrinsic dimension is known a priori. The discrete ET algorithm is computationally efficient, being on the order of O(MN) using N measuring distances across an image of M ridge pixels. It involves easy and few parameter settings. We demonstrate and assess the performance of ET on the automatic detection of the prostate locations from supra-pubic ultrasound images. ET yields superior results in terms of positive detection rate, accuracy and coverage.

Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy.

PubMed

Fernandes, Jill N; Dos Santos, Lílha M B; Chouin-Carneiro, Thaís; Pavan, Márcio G; Garcia, Gabriela A; David, Mariana R; Beier, John C; Dowell, Floyd E; Maciel-de-Freitas, Rafael; Sikulu-Lord, Maggy T

2018-05-01

The accelerating global spread of arboviruses, such as Zika virus (ZIKV), highlights the need for more proactive mosquito surveillance. However, a major challenge during arbovirus outbreaks has been the lack of rapid and affordable tests for pathogen detection in mosquitoes. We show for the first time that near-infrared spectroscopy (NIRS) is a rapid, reagent-free, and cost-effective tool that can be used to noninvasively detect ZIKV in heads and thoraces of intact Aedes aegypti mosquitoes with prediction accuracies of 94.2 to 99.3% relative to quantitative reverse transcription polymerase chain reaction (RT-qPCR). NIRS involves simply shining a beam of light on a mosquito to collect a diagnostic spectrum. We estimated in this study that NIRS is 18 times faster and 110 times cheaper than RT-qPCR. We anticipate that NIRS will be expanded upon for identifying potential arbovirus hotspots to guide the spatial prioritization of vector control.

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

NASA Astrophysics Data System (ADS)

Mirik, Mustafa; Ansley, R. James; Steddom, Karl; Rush, Charles M.; Michels, Gerald J.; Workneh, Fekede; Cui, Song; Elliott, Norman C.

2014-01-01

The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect pest that causes damage to wheat (Triticum aestivum L.). We proposed that concentrated RWA feeding areas, referred to as "hot spots," could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of >89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in wheat for site-specific aphid management.

Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution.

PubMed

Peña, José M; Torres-Sánchez, Jorge; Serrano-Pérez, Angélica; de Castro, Ana I; López-Granados, Francisca

2015-03-06

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5-6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations.

Optoacoustic Monitoring of Physiologic Variables

PubMed Central

Esenaliev, Rinat O.

2017-01-01

Optoacoustic (photoacoustic) technique is a novel diagnostic platform that can be used for noninvasive measurements of physiologic variables, functional imaging, and hemodynamic monitoring. This technique is based on generation and time-resolved detection of optoacoustic (thermoelastic) waves generated in tissue by short optical pulses. This provides probing of tissues and individual blood vessels with high optical contrast and ultrasound spatial resolution. Because the optoacoustic waves carry information on tissue optical and thermophysical properties, detection, and analysis of the optoacoustic waves allow for measurements of physiologic variables with high accuracy and specificity. We proposed to use the optoacoustic technique for monitoring of a number of important physiologic variables including temperature, thermal coagulation, freezing, concentration of molecular dyes, nanoparticles, oxygenation, and hemoglobin concentration. In this review we present origin of contrast and high spatial resolution in these measurements performed with optoacoustic systems developed and built by our group. We summarize data obtained in vitro, in experimental animals, and in humans on monitoring of these physiologic variables. Our data indicate that the optoacoustic technology may be used for monitoring of cerebral blood oxygenation in patients with traumatic brain injury and in neonatal patients, central venous oxygenation monitoring, total hemoglobin concentration monitoring, hematoma detection and characterization, monitoring of temperature, and coagulation and freezing boundaries during thermotherapy. PMID:29311964

Optoacoustic Monitoring of Physiologic Variables.

PubMed

Esenaliev, Rinat O

2017-01-01

Optoacoustic (photoacoustic) technique is a novel diagnostic platform that can be used for noninvasive measurements of physiologic variables, functional imaging, and hemodynamic monitoring. This technique is based on generation and time-resolved detection of optoacoustic (thermoelastic) waves generated in tissue by short optical pulses. This provides probing of tissues and individual blood vessels with high optical contrast and ultrasound spatial resolution. Because the optoacoustic waves carry information on tissue optical and thermophysical properties, detection, and analysis of the optoacoustic waves allow for measurements of physiologic variables with high accuracy and specificity. We proposed to use the optoacoustic technique for monitoring of a number of important physiologic variables including temperature, thermal coagulation, freezing, concentration of molecular dyes, nanoparticles, oxygenation, and hemoglobin concentration. In this review we present origin of contrast and high spatial resolution in these measurements performed with optoacoustic systems developed and built by our group. We summarize data obtained in vitro , in experimental animals, and in humans on monitoring of these physiologic variables. Our data indicate that the optoacoustic technology may be used for monitoring of cerebral blood oxygenation in patients with traumatic brain injury and in neonatal patients, central venous oxygenation monitoring, total hemoglobin concentration monitoring, hematoma detection and characterization, monitoring of temperature, and coagulation and freezing boundaries during thermotherapy.

Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution

PubMed Central

Peña, José M.; Torres-Sánchez, Jorge; Serrano-Pérez, Angélica; de Castro, Ana I.; López-Granados, Francisca

2015-01-01

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations. PMID:25756867

The perceptual saliency of fearful eyes and smiles: A signal detection study

PubMed Central

Saban, Muhammet Ikbal; Rotshtein, Pia

2017-01-01

Facial features differ in the amount of expressive information they convey. Specifically, eyes are argued to be essential for fear recognition, while smiles are crucial for recognising happy expressions. In three experiments, we tested whether expression modulates the perceptual saliency of diagnostic facial features and whether the feature’s saliency depends on the face configuration. Participants were presented with masked facial features or noise at perceptual conscious threshold. The task was to indicate whether eyes (experiments 1-3A) or a mouth (experiment 3B) was present. The expression of the face and its configuration (i.e. spatial arrangement of the features) were manipulated. Experiment 1 compared fearful with neutral expressions, experiments 2 and 3 compared fearful versus happy expressions. The detection accuracy data was analysed using Signal Detection Theory (SDT), to examine the effects of expression and configuration on perceptual precision (d’) and response bias (c), separately. Across all three experiments, fearful eyes were detected better (higher d’) than neutral and happy eyes. Eyes were more precisely detected than mouths, whereas smiles were detected better than fearful mouths. The configuration of the features had no consistent effects across the experiments on the ability to detect expressive features. But facial configuration affected consistently the response bias. Participants used a more liberal criterion for detecting the eyes in canonical configuration and fearful expression. Finally, the power in low spatial frequency of a feature predicted its discriminability index. The results suggest that expressive features are perceptually more salient with a higher d’ due to changes at the low-level visual properties, with emotions and configuration affecting perception through top-down processes, as reflected by the response bias. PMID:28267761

Spatial decoupling of targets and flashing stimuli for visual brain-computer interfaces

NASA Astrophysics Data System (ADS)

Waytowich, Nicholas R.; Krusienski, Dean J.

2015-06-01

Objective. Recently, paradigms using code-modulated visual evoked potentials (c-VEPs) have proven to achieve among the highest information transfer rates for noninvasive brain-computer interfaces (BCIs). One issue with current c-VEP paradigms, and visual-evoked paradigms in general, is that they require direct foveal fixation of the flashing stimuli. These interfaces are often visually unpleasant and can be irritating and fatiguing to the user, thus adversely impacting practical performance. In this study, a novel c-VEP BCI paradigm is presented that attempts to perform spatial decoupling of the targets and flashing stimuli using two distinct concepts: spatial separation and boundary positioning. Approach. For the paradigm, the flashing stimuli form a ring that encompasses the intended non-flashing targets, which are spatially separated from the stimuli. The user fixates on the desired target, which is classified using the changes to the EEG induced by the flashing stimuli located in the non-foveal visual field. Additionally, a subset of targets is also positioned at or near the stimulus boundaries, which decouples targets from direct association with a single stimulus. This allows a greater number of target locations for a fixed number of flashing stimuli. Main results. Results from 11 subjects showed practical classification accuracies for the non-foveal condition, with comparable performance to the direct-foveal condition for longer observation lengths. Online results from 5 subjects confirmed the offline results with an average accuracy across subjects of 95.6% for a 4-target condition. The offline analysis also indicated that targets positioned at or near the boundaries of two stimuli could be classified with the same accuracy as traditional superimposed (non-boundary) targets. Significance. The implications of this research are that c-VEPs can be detected and accurately classified to achieve comparable BCI performance without requiring potentially irritating direct foveation of flashing stimuli. Furthermore, this study shows that it is possible to increase the number of targets beyond the number of stimuli without degrading performance. Given the superior information transfer rate of c-VEP paradigms, these results can lead to the development of more practical and ergonomic BCIs.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

PubMed

Qiao, Pengwei; Lei, Mei; Yang, Sucai; Yang, Jun; Guo, Guanghui; Zhou, Xiaoyong

2018-06-01

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Development of image processing method to detect noise in geostationary imagery

NASA Astrophysics Data System (ADS)

Khlopenkov, Konstantin V.; Doelling, David R.

2016-10-01

The Clouds and the Earth's Radiant Energy System (CERES) has incorporated imagery from 16 individual geostationary (GEO) satellites across five contiguous domains since March 2000. In order to derive broadband fluxes uniform across satellite platforms it is important to ensure a good quality of the input raw count data. GEO data obtained by older GOES imagers (such as MTSAT-1, Meteosat-5, Meteosat-7, GMS-5, and GOES-9) are known to frequently contain various types of noise caused by transmission errors, sync errors, stray light contamination, and others. This work presents an image processing methodology designed to detect most kinds of noise and corrupt data in all bands of raw imagery from modern and historic GEO satellites. The algorithm is based on a set of different approaches to detect abnormal image patterns, including inter-line and inter-pixel differences within a scanline, correlation between scanlines, analysis of spatial variance, and also a 2D Fourier analysis of the image spatial frequencies. In spite of computational complexity, the described method is highly optimized for performance to facilitate volume processing of multi-year data and runs in fully automated mode. Reliability of this noise detection technique has been assessed by human supervision for each GEO dataset obtained during selected time periods in 2005 and 2006. This assessment has demonstrated the overall detection accuracy of over 99.5% and the false alarm rate of under 0.3%. The described noise detection routine is currently used in volume processing of historical GEO imagery for subsequent production of global gridded data products and for cross-platform calibration.

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

NASA Astrophysics Data System (ADS)

Caras, Tamir; Hedley, John; Karnieli, Arnon

2017-12-01

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.

Haptic discrimination of bilateral symmetry in 2-dimensional and 3-dimensional unfamiliar displays.

PubMed

Ballesteros, S; Manga, D; Reales, J M

1997-01-01

In five experiments, we tested the accuracy and sensitivity of the haptic system in detecting bilateral symmetry of raised-line shapes (Experiments 1 and 2) and unfamiliar 3-D objects (Experiments 3-5) under different time constraints and different modes of exploration. Touch was moderately accurate for detecting this property in raised displays. Experiment 1 showed that asymmetric judgments were systematically more accurate than were symmetric judgements with scanning by one finger. Experiments 2 confirmed the results of Experiment 1 but also showed that bimanual exploration facilitated processing of symmetric shapes without improving asymmetric detections. Bimanual exploration of 3-D objects was very accurate and significantly facilitated processing of symmetric objects under different time constraints (Experiment 3). Unimanual exploration did not differ from bimanual exploration (Experiment 4), but restricting hand movements to one enclosure reduced performance significantly (Experiment 5). Spatial reference information, signal detection measures, and hand movements in processing bilateral symmetry by touch are discussed.

Contact detection for nanomanipulation in a scanning electron microscope.

PubMed

Ru, Changhai; To, Steve

2012-07-01

Nanomanipulation systems require accurate knowledge of the end-effector position in all three spatial coordinates, XYZ, for reliable manipulation of nanostructures. Although the images acquired by a scanning electron microscope (SEM) provide high resolution XY information, the lack of depth information in the Z-direction makes 3D nanomanipulation time-consuming. Existing approaches for contact detection of end-effectors inside SEM typically utilize fragile touch sensors that are difficult to integrate into a nanomanipulation system. This paper presents a method for determining the contact between an end-effector and a target surface during nanomanipulation inside SEM, purely based on the processing of SEM images. A depth-from-focus method is used in the fast approach of the end-effector to the substrate, followed by fine contact detection. Experimental results demonstrate that the contact detection approach is capable of achieving an accuracy of 21.5 nm at 50,000× magnification while inducing little end-effector damage. Copyright © 2012 Elsevier B.V. All rights reserved.

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

PubMed

Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, And Moongu

2018-06-12

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall ( with 2 classes and 3 classes ) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems

PubMed Central

Yue, Dan; Xu, Shuyan; Nie, Haitao; Wang, Zongyang

2016-01-01

The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality. PMID:26934045

Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

Treesearch

Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...

Ensemble coding remains accurate under object and spatial visual working memory load.

PubMed

Epstein, Michael L; Emmanouil, Tatiana A

2017-10-01

A number of studies have provided evidence that the visual system statistically summarizes large amounts of information that would exceed the limitations of attention and working memory (ensemble coding). However the necessity of working memory resources for ensemble coding has not yet been tested directly. In the current study, we used a dual task design to test the effect of object and spatial visual working memory load on size averaging accuracy. In Experiment 1, we tested participants' accuracy in comparing the mean size of two sets under various levels of object visual working memory load. Although the accuracy of average size judgments depended on the difference in mean size between the two sets, we found no effect of working memory load. In Experiment 2, we tested the same average size judgment while participants were under spatial visual working memory load, again finding no effect of load on averaging accuracy. Overall our results reveal that ensemble coding can proceed unimpeded and highly accurately under both object and spatial visual working memory load, providing further evidence that ensemble coding reflects a basic perceptual process distinct from that of individual object processing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brewster, S.B.

The U.S. Department of Energy's Remote Sensing Laboratory developed the geometric correction system (GCS) as a state-of-the-art solution for removing distortions from multispectral line scanner data caused by aircraft motion. The system operates on Daedalus AADS-1268 scanner data acquired from fixed-wing and helicopter platforms. The aircraft attitude, altitude, acceleration, and location are recorded and applied to the data, thereby determining the location of the earth with respect to a given datum and projection. The GCS has yielded a positional accuracy of 0.5 meters when used with a 1-meter digital elevation model. Data at this level of accuracy are invaluable inmore » making precise areal estimates and as input into a geographic information system. The combination of high-spatial resolution and accurate geo-rectification makes the GCS a unique tool in identifying and locating environmental conditions, finding targets of interest, and detecting changes as they occur over time.« less

Visual gate for brain-computer interfaces.

PubMed

Dias, N S; Jacinto, L R; Mendes, P M; Correia, J H

2009-01-01

Brain-Computer Interfaces (BCI) based on event related potentials (ERP) have been successfully developed for applications like virtual spellers and navigation systems. This study tests the use of visual stimuli unbalanced in the subject's field of view to simultaneously cue mental imagery tasks (left vs. right hand movement) and detect subject attention. The responses to unbalanced cues were compared with the responses to balanced cues in terms of classification accuracy. Subject specific ERP spatial filters were calculated for optimal group separation. The unbalanced cues appear to enhance early ERPs related to cue visuospatial processing that improved the classification accuracy (as low as 6%) of ERPs in response to left vs. right cues soon (150-200 ms) after the cue presentation. This work suggests that such visual interface may be of interest in BCI applications as a gate mechanism for attention estimation and validation of control decisions.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

High sensitive and high temporal and spatial resolved image of reactive species in atmospheric pressure surface discharge reactor by laser induced fluorescence

NASA Astrophysics Data System (ADS)

Gao, Liang; Feng, Chun-Lei; Wang, Zhi-Wei; Ding, Hongbin

2017-05-01

The current paucity of spatial and temporal characterization of reactive oxygen and nitrogen species (RONS) concentration has been a major hurdle to the advancement and clinical translation of low temperature atmospheric plasmas. In this study, an advanced laser induced fluorescence (LIF) system has been developed to be an effective antibacterial surface discharge reactor for the diagnosis of RONS, where the highest spatial and temporal resolution of the LIF system has been achieved to ˜100 μm scale and ˜20 ns scale, respectively. Measurements on an oxidative OH radical have been carried out as typical RONS for the benchmark of the whole LIF system, where absolute number density calibration has been performed on the basis of the laser Rayleigh scattering method. Requirements for pixel resolved spatial distribution and outer plasma region detection become challenging tasks due to the low RONS concentration (˜ppb level) and strong interference, especially the discharge induced emission and pulsed laser induced stray light. In order to design the highly sensitive LIF system, a self-developed fluorescence telescope, the optimization of high precision synchronization among a tunable pulsed laser, a surface discharge generator, intensified Charge Coupled Device (iCCD) camera, and an oscilloscope have been performed. Moreover, an image BOXCAR approach has been developed to remarkably improve the sensitivity of the whole LIF system by optimizing spatial and temporal gating functions via both hardware and software, which has been integrated into our automatic control and data acquisition system on the LabVIEW platform. In addition, a reciprocation averaging measurement has been applied to verify the accuracy of the whole LIF detecting system, indicating the relative standard deviation of ˜3%.

Speed of sound and photoacoustic imaging with an optical camera based ultrasound detection system

NASA Astrophysics Data System (ADS)

Nuster, Robert; Paltauf, Guenther

2017-07-01

CCD camera based optical ultrasound detection is a promising alternative approach for high resolution 3D photoacoustic imaging (PAI). To fully exploit its potential and to achieve an image resolution <50 μm, it is necessary to incorporate variations of the speed of sound (SOS) in the image reconstruction algorithm. Hence, in the proposed work the idea and a first implementation are shown how speed of sound imaging can be added to a previously developed camera based PAI setup. The current setup provides SOS-maps with a spatial resolution of 2 mm and an accuracy of the obtained absolute SOS values of about 1%. The proposed dual-modality setup has the potential to provide highly resolved and perfectly co-registered 3D photoacoustic and SOS images.

First steps towards dual-modality 3D photoacoustic and speed of sound imaging with optical ultrasound detection

NASA Astrophysics Data System (ADS)

Nuster, Robert; Wurzinger, Gerhild; Paltauf, Guenther

2017-03-01

CCD camera based optical ultrasound detection is a promising alternative approach for high resolution 3D photoacoustic imaging (PAI). To fully exploit its potential and to achieve an image resolution <50 μm, it is necessary to incorporate variations of the speed of sound (SOS) in the image reconstruction algorithm. Hence, in the proposed work the idea and a first implementation are shown how speed of sound imaging can be added to a previously developed camera based PAI setup. The current setup provides SOS-maps with a spatial resolution of 2 mm and an accuracy of the obtained absolute SOS values of about 1%. The proposed dual-modality setup has the potential to provide highly resolved and perfectly co-registered 3D photoacoustic and SOS images.

ACCURACY OF THE 1992 NATIONAL LAND COVER DATASET AREA ESTIMATES: AN ANALYSIS AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Abstract for poster presentation:

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover(LULC) datasets but provide little insight into accuracy of area estimates of LULC

classes derived from sampling units of varying size. Additiona...

Endogenous spatial attention: evidence for intact functioning in adults with autism

PubMed Central

Grubb, Michael A.; Behrmann, Marlene; Egan, Ryan; Minshew, Nancy J.; Carrasco, Marisa; Heeger, David J.

2012-01-01

Lay Abstract Attention allows us to selectively process the vast amount of information with which we are confronted. Focusing on a certain location of the visual scene (visual spatial attention) enables the prioritization of some aspects of information while ignoring others. Rapid manipulation of the attention field (i.e., the location and spread of visual spatial attention) is a critical aspect of human cognition, and previous research on spatial attention in individuals with autism spectrum disorders (ASD) has produced inconsistent results. In a series of three experiments, we evaluated claims in the literature that individuals with ASD exhibit a deficit in voluntarily controlling the deployment and size of the spatial attention field. We measured how well participants perform a visual discrimination task (accuracy) and how quickly they do so (reaction time), with and without spatial uncertainty (i.e., the lack of predictability concerning the spatial position of the upcoming stimulus). We found that high–functioning adults with autism exhibited slower reactions times overall with spatial uncertainty, but the effects of attention on performance accuracies and reaction times were indistinguishable between individuals with autism and typically developing individuals, in all three experiments. These results provide evidence of intact endogenous spatial attention function in high–functioning adults with ASD, suggesting that atypical endogenous spatial attention cannot be a latent characteristic of autism in general. Scientific Abstract Rapid manipulation of the attention field (i.e., the location and spread of visual spatial attention) is a critical aspect of human cognition, and previous research on spatial attention in individuals with autism spectrum disorders (ASD) has produced inconsistent results. In a series of three psychophysical experiments, we evaluated claims in the literature that individuals with ASD exhibit a deficit in voluntarily controlling the deployment and size of the spatial attention field. We measured the spatial distribution of performance accuracies and reaction times to quantify the sizes and locations of the attention field, with and without spatial uncertainty (i.e., the lack of predictability concerning the spatial position of the upcoming stimulus). We found that high–functioning adults with autism exhibited slower reactions times overall with spatial uncertainty, but the effects of attention on performance accuracies and reaction times were indistinguishable between individuals with autism and typically developing individuals, in all three experiments. These results provide evidence of intact endogenous spatial attention function in high–functioning adults with ASD, suggesting that atypical endogenous attention cannot be a latent characteristic of autism in general. PMID:23427075

Can grapheme-color synesthesia be induced by hypnosis?

PubMed Central

Anderson, Hazel P.; Seth, Anil K.; Dienes, Zoltan; Ward, Jamie

2014-01-01

Grapheme-color synesthesia is a perceptual experience where graphemes, letters or words evoke a specific color, which are experienced either as spatially coincident with the grapheme inducer (projector sub-type) or elsewhere, perhaps without a definite spatial location (associator sub-type). Here, we address the question of whether synesthesia can be rapidly produced using a hypnotic color suggestion to examine the possibility of “hypnotic synesthesia”, i.e., subjectively experienced color hallucinations similar to those experienced by projector synesthetes. We assess the efficacy of this intervention using an “embedded figures” test, in which participants are required to detect a shape (e.g., a square) composed of local graphemic elements. For grapheme-color synesthetes, better performance on the task has been linked to a higher proportion of graphemes perceived as colored. We found no performance benefits on this test when using a hypnotic suggestion, as compared to a no-suggestion control condition. The same result was found when participants were separated according to the degree to which they were susceptible to the suggestion (number of colored trials perceived). However, we found a relationship between accuracy and subjective reports of color in those participants who reported a large proportion of colored trials: trials in which the embedded figure was accurately recognized (relative to trials in which it was not) were associated with reports of more intense colors occupying a greater spatial extent. Collectively, this implies that hypnotic color was only perceived after shape detection rather than aiding in shape detection via color-based perceptual grouping. The results suggest that hypnotically induced colors are not directly comparable to synesthetic ones. PMID:24829555

Three dimensional time reversal optical tomography

NASA Astrophysics Data System (ADS)

Wu, Binlin; Cai, W.; Alrubaiee, M.; Xu, M.; Gayen, S. K.

2011-03-01

Time reversal optical tomography (TROT) approach is used to detect and locate absorptive targets embedded in a highly scattering turbid medium to assess its potential in breast cancer detection. TROT experimental arrangement uses multi-source probing and multi-detector signal acquisition and Multiple-Signal-Classification (MUSIC) algorithm for target location retrieval. Light transport from multiple sources through the intervening medium with embedded targets to the detectors is represented by a response matrix constructed using experimental data. A TR matrix is formed by multiplying the response matrix by its transpose. The eigenvectors with leading non-zero eigenvalues of the TR matrix correspond to embedded objects. The approach was used to: (a) obtain the location and spatial resolution of an absorptive target as a function of its axial position between the source and detector planes; and (b) study variation in spatial resolution of two targets at the same axial position but different lateral positions. The target(s) were glass sphere(s) of diameter ~9 mm filled with ink (absorber) embedded in a 60 mm-thick slab of Intralipid-20% suspension in water with an absorption coefficient μa ~ 0.003 mm-1 and a transport mean free path lt ~ 1 mm at 790 nm, which emulate the average values of those parameters for human breast tissue. The spatial resolution and accuracy of target location depended on axial position, and target contrast relative to the background. Both the targets could be resolved and located even when they were only 4-mm apart. The TROT approach is fast, accurate, and has the potential to be useful in breast cancer detection and localization.

Correction of Spatial Bias in Oligonucleotide Array Data

PubMed Central

Lemieux, Sébastien

2013-01-01

Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. PMID:23573083

Cryogenic colocalization microscopy for nanometer-distance measurements.

PubMed

Weisenburger, Siegfried; Jing, Bo; Hänni, Dominik; Reymond, Luc; Schuler, Benjamin; Renn, Alois; Sandoghdar, Vahid

2014-03-17

The main limiting factor in spatial resolution of localization microscopy is the number of detected photons. Recently we showed that cryogenic measurements improve the photostability of fluorophores, giving access to Angstrom precision in localization of single molecules. Here, we extend this method to colocalize two fluorophores attached to well-defined positions of a double-stranded DNA. By measuring the separations of the fluorophore pairs prepared at different design positions, we verify the feasibility of cryogenic distance measurement with sub-nanometer accuracy. We discuss the important challenges of our method as well as its potential for further improvement and various applications.

Distributed dynamic large strain optical fiber sensor based on the detection of spontaneous Brillouin scattering.

PubMed

Masoudi, Ali; Belal, Mohammad; Newson, Trevor P

2013-09-01

A Brillouin-based distributed optical fiber dynamic strain sensor is described which converts strain-induced Brillouin frequency shift into optical intensity variations by using an imbalanced Mach-Zhender interferometer. A 3×3 coupler is used at the output of this interferometer to permit differentiate and cross multiply demodulation. The demonstrated sensor is capable of probing dynamic strain disturbances over 2 km of sensing length every 0.5 s up to a strain of 10 mε with an accuracy of ±50 με and spatial resolution of 1.3 m.

Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

2013-05-01

Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lafata, K; Ren, L; Cai, J

2016-06-15

Purpose: To develop a methodology based on digitally-reconstructed-fluoroscopy (DRF) to quantitatively assess target localization accuracy of lung SBRT, and to evaluate using both a dynamic digital phantom and a patient dataset. Methods: For each treatment field, a 10-phase DRF is generated based on the planning 4DCT. Each frame is pre-processed with a morphological top-hat filter, and corresponding beam apertures are projected to each detector plane. A template-matching algorithm based on cross-correlation is used to detect the tumor location in each frame. Tumor motion relative beam aperture is extracted in the superior-inferior direction based on each frame’s impulse response to themore » template, and the mean tumor position (MTP) is calculated as the average tumor displacement. The DRF template coordinates are then transferred to the corresponding MV-cine dataset, which is retrospectively filtered as above. The treatment MTP is calculated within each field’s projection space, relative to the DRF-defined template. The field’s localization error is defined as the difference between the DRF-derived-MTP (planning) and the MV-cine-derived-MTP (delivery). A dynamic digital phantom was used to assess the algorithm’s ability to detect intra-fractional changes in patient alignment, by simulating different spatial variations in the MV-cine and calculating the corresponding change in MTP. Inter-and-intra-fractional variation, IGRT accuracy, and filtering effects were investigated on a patient dataset. Results: Phantom results demonstrated a high accuracy in detecting both translational and rotational variation. The lowest localization error of the patient dataset was achieved at each fraction’s first field (mean=0.38mm), with Fx3 demonstrating a particularly strong correlation between intra-fractional motion-caused localization error and treatment progress. Filtering significantly improved tracking visibility in both the DRF and MV-cine images. Conclusion: We have developed and evaluated a methodology to quantify lung SBRT target localization accuracy based on digitally-reconstructed-fluoroscopy. Our approach may be useful in potentially reducing treatment margins to optimize lung SBRT outcomes. R01-184173.« less

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Automated discrimination of lower and higher grade gliomas based on histopathological image analysis.

PubMed

Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, Arvind U K

2015-01-01

Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

Visuospatial selective attention in chickens.

PubMed

Sridharan, Devarajan; Ramamurthy, Deepa L; Schwarz, Jason S; Knudsen, Eric I

2014-05-13

Voluntary control of attention promotes intelligent, adaptive behaviors by enabling the selective processing of information that is most relevant for making decisions. Despite extensive research on attention in primates, the capacity for selective attention in nonprimate species has never been quantified. Here we demonstrate selective attention in chickens by applying protocols that have been used to characterize visual spatial attention in primates. Chickens were trained to localize and report the vertical position of a target in the presence of task-relevant distracters. A spatial cue, the location of which varied across individual trials, indicated the horizontal, but not vertical, position of the upcoming target. Spatial cueing improved localization performance: accuracy (d') increased and reaction times decreased in a space-specific manner. Distracters severely impaired perceptual performance, and this impairment was greatly reduced by spatial cueing. Signal detection analysis with an "indecision" model demonstrated that spatial cueing significantly increased choice certainty in localizing targets. By contrast, error-aversion certainty (certainty of not making an error) remained essentially constant across cueing protocols, target contrasts, and individuals. The results show that chickens shift spatial attention rapidly and dynamically, following principles of stimulus selection that closely parallel those documented in primates. The findings suggest that the mechanisms that control attention have been conserved through evolution, and establish chickens--a highly visual species that is easily trained and amenable to cutting-edge experimental technologies--as an attractive model for linking behavior to neural mechanisms of selective attention.

Quantifying Structural and Compositional Changes in Forest Cover in NW Yunnan, China

NASA Astrophysics Data System (ADS)

Hakkenberg, C.

2012-12-01

NW Yunnan, China is a region renowned for high levels of biodiversity, endemism and genetically distinct refugial plant populations. It is also a focal area for China's national reforestation efforts like the Natural Forest Protection Program (NFPP), intended to control erosion in the Upper Yangtze watershed. As part of a larger project to investigate the role of reforestation programs in facilitating the emergence of increasingly species-rich forest communities on a previously degraded and depauperate land mosaic in montane SW China, this study uses a series of Landsat TM images to quantify the spatial pattern and rate of structural and compositional change in forests recovering from medium to large-scale disturbances in the area over the past 25 years. Beyond the fundamental need to assess the outcomes of one of the world's largest reforestation programs, this research offers approaches to confronting two critical methodological issues: (1) techniques for characterizing subtle changes in the nature of vegetation cover, and (2) reducing change detection uncertainty due to persistent cloud cover and shadow. To address difficulties in accurately assessing the structure and composition of vegetative regrowth, a biophysical model was parameterized with over 300 ground-truthed canopy cover assessment points to determine pattern and rate of long-term vegetation changes. To combat pervasive shadow and cloud cover, an interactive generalized additive model (GAM) model based on topographic and spatial predictors was used to overcome some of the constraints of satellite image analysis in Himalayan regions characterized by extreme topography and extensive cloud cover during the summer monsoon. The change detection is assessed for accuracy using ground-truthed observations in a variety of forest cover types and topographic positions. Results indicate effectiveness in reducing the areal extent of unclassified regions and increasing total change detection accuracy. In addition to quantifying forest cover change in this section of NW Yunnan, the analysis attempts to qualify that change - distinguishing among distinct disturbance histories and post-recovery successional pathways.

Robust vehicle detection under various environments to realize road traffic flow surveillance using an infrared thermal camera.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.

Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized. PMID:25763384

Detection of bio-signature by microscopy and mass spectrometry

NASA Astrophysics Data System (ADS)

Tulej, M.; Wiesendanger, R.; Neuland, M., B.; Meyer, S.; Wurz, P.; Neubeck, A.; Ivarsson, M.; Riedo, V.; Moreno-Garcia, P.; Riedo, A.; Knopp, G.

2017-09-01

We demonstrate detection of micro-sized fossilized bacteria by means of microscopy and mass spectrometry. The characteristic structures of lifelike forms are visualized with a micrometre spatial resolution and mass spectrometric analyses deliver elemental and isotope composition of host and fossilized materials. Our studies show that high selectivity in isolation of fossilized material from host phase can be achieved while applying a microscope visualization (location), a laser ablation ion source with sufficiently small laser spot size and applying depth profiling method. Our investigations shows that fossilized features can be well isolated from host phase. The mass spectrometric measurements can be conducted with sufficiently high accuracy and precision yielding quantitative elemental and isotope composition of micro-sized objects. The current performance of the instrument allows the measurement of the isotope fractionation in per mill level and yield exclusively definition of the origin of the investigated species by combining optical visualization of investigated samples (morphology and texture), chemical characterization of host and embedded in the host micro-sized structure. Our isotope analyses involved bio-relevant B, C, S, and Ni isotopes which could be measured with sufficiently accuracy to conclude about the nature of the micro-sized objects.

Web Service for Positional Quality Assessment: the Wps Tier

NASA Astrophysics Data System (ADS)

Xavier, E. M. A.; Ariza-López, F. J.; Ureña-Cámara, M. A.

2015-08-01

In the field of spatial data every day we have more and more information available, but we still have little or very little information about the quality of spatial data. We consider that the automation of the spatial data quality assessment is a true need for the geomatic sector, and that automation is possible by means of web processing services (WPS), and the application of specific assessment procedures. In this paper we propose and develop a WPS tier centered on the automation of the positional quality assessment. An experiment using the NSSDA positional accuracy method is presented. The experiment involves the uploading by the client of two datasets (reference and evaluation data). The processing is to determine homologous pairs of points (by distance) and calculate the value of positional accuracy under the NSSDA standard. The process generates a small report that is sent to the client. From our experiment, we reached some conclusions on the advantages and disadvantages of WPSs when applied to the automation of spatial data accuracy assessments.

Going the distance: spatial scale of athletic experience affects the accuracy of path integration.

PubMed

Smith, Alastair D; Howard, Christina J; Alcock, Niall; Cater, Kirsten

2010-09-01

Evidence suggests that athletically trained individuals are more accurate than untrained individuals in updating their spatial position through idiothetic cues. We assessed whether training at different spatial scales affects the accuracy of path integration. Groups of rugby players (large-scale training) and martial artists (small-scale training) participated in a triangle-completion task: they were led (blindfolded) along two sides of a right-angled triangle and were required to complete the hypotenuse by returning to the origin. The groups did not differ in their assessment of the distance to the origin, but rugby players were more accurate than martial artists in assessing the correct angle to turn (heading), and landed significantly closer to the origin. These data support evidence that distance and heading components can be dissociated. Furthermore, they suggest that the spatial scale at which an individual is trained may affect the accuracy of one component of path integration but not the other.

Evaluation of spatial filtering on the accuracy of wheat area estimate

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.

1982-01-01

A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Secured network sensor-based defense system

NASA Astrophysics Data System (ADS)

Wei, Sixiao; Shen, Dan; Ge, Linqiang; Yu, Wei; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe

2015-05-01

Network sensor-based defense (NSD) systems have been widely used to defend against cyber threats. Nonetheless, if the adversary finds ways to identify the location of monitor sensors, the effectiveness of NSD systems can be reduced. In this paper, we propose both temporal and spatial perturbation based defense mechanisms to secure NSD systems and make the monitor sensor invisible to the adversary. The temporal-perturbation based defense manipulates the timing information of published data so that the probability of successfully recognizing monitor sensors can be reduced. The spatial-perturbation based defense dynamically redeploys monitor sensors in the network so that the adversary cannot obtain the complete information to recognize all of the monitor sensors. We carried out experiments using real-world traffic traces to evaluate the effectiveness of our proposed defense mechanisms. Our data shows that our proposed defense mechanisms can reduce the attack accuracy of recognizing detection sensors.

A novel SPECT camera for molecular imaging of the prostate

NASA Astrophysics Data System (ADS)

Cebula, Alan; Gilland, David; Su, Li-Ming; Wagenaar, Douglas; Bahadori, Amir

2011-10-01

The objective of this work is to develop an improved SPECT camera for dedicated prostate imaging. Complementing the recent advancements in agents for molecular prostate imaging, this device has the potential to assist in distinguishing benign from aggressive cancers, to improve site-specific localization of cancer, to improve accuracy of needle-guided prostate biopsy of cancer sites, and to aid in focal therapy procedures such as cryotherapy and radiation. Theoretical calculations show that the spatial resolution/detection sensitivity of the proposed SPECT camera can rival or exceed 3D PET and further signal-to-noise advantage is attained with the better energy resolution of the CZT modules. Based on photon transport simulation studies, the system has a reconstructed spatial resolution of 4.8 mm with a sensitivity of 0.0001. Reconstruction of a simulated prostate distribution demonstrates the focal imaging capability of the system.

Investigation of LANDSAT follow-on thematic mapper spatial, radiometric and spectral resolution

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Morgenstern, J. P.; Kent, E. R.; Erickson, J. D.

1976-01-01

The author has identified the following significant results. Fine resolution M7 multispectral scanner data collected during the Corn Blight Watch Experiment in 1971 served as the basis for this study. Different locations and times of year were studied. Definite improvement using 30-40 meter spatial resolution over present LANDSAT 1 resolution and over 50-60 meter resolution was observed, using crop area mensuration as the measure. Simulation studies carried out to extrapolate the empirical results to a range of field size distributions confirmed this effect, showing the improvement to be most pronounced for field sizes of 1-4 hectares. Radiometric sensitivity study showed significant degradation of crop classification accuracy immediately upon relaxation from the nominally specified values of 0.5% noise equivalent reflectance. This was especially the case for data which were spectrally similar such as that collected early in the growing season and also when attempting to accomplish crop stress detection.

Spatiotemporal attention operator using isotropic contrast and regional homogeneity

NASA Astrophysics Data System (ADS)

Palenichka, Roman; Lakhssassi, Ahmed; Zaremba, Marek

2011-04-01

A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.

Harmonic motion detection in a vibrating scattering medium.

PubMed

Urban, Matthew W; Chen, Shigao; Greenleaf, James

2008-09-01

Elasticity imaging is an emerging medical imaging modality that seeks to map the spatial distribution of tissue stiffness. Ultrasound radiation force excitation and motion tracking using pulse-echo ultrasound have been used in numerous methods. Dynamic radiation force is used in vibrometry to cause an object or tissue to vibrate, and the vibration amplitude and phase can be measured with exceptional accuracy. This paper presents a model that simulates harmonic motion detection in a vibrating scattering medium incorporating 3-D beam shapes for radiation force excitation and motion tracking. A parameterized analysis using this model provides a platform to optimize motion detection for vibrometry applications in tissue. An experimental method that produces a multifrequency radiation force is also presented. Experimental harmonic motion detection of simultaneous multifrequency vibration is demonstrated using a single transducer. This method can accurately detect motion with displacement amplitude as low as 100 to 200 nm in bovine muscle. Vibration phase can be measured within 10 degrees or less. The experimental results validate the conclusions observed from the model and show multifrequency vibration induction and measurements can be performed simultaneously.

Classification with an edge: Improving semantic image segmentation with boundary detection

NASA Astrophysics Data System (ADS)

Marmanis, D.; Schindler, K.; Wegner, J. D.; Galliani, S.; Datcu, M.; Stilla, U.

2018-01-01

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the SEGNET encoder-decoder architecture. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.

Harmonic Motion Detection in a Vibrating Scattering Medium

PubMed Central

Urban, Matthew W.; Chen, Shigao; Greenleaf, James F.

2008-01-01

Elasticity imaging is an emerging medical imaging modality that seeks to map the spatial distribution of tissue stiffness. Ultrasound radiation force excitation and motion tracking using pulse-echo ultrasound have been used in numerous methods. Dynamic radiation force is used in vibrometry to cause an object or tissue to vibrate, and the vibration amplitude and phase can be measured with exceptional accuracy. This paper presents a model that simulates harmonic motion detection in a vibrating scattering medium incorporating 3-D beam shapes for radiation force excitation and motion tracking. A parameterized analysis using this model provides a platform to optimize motion detection for vibrometry applications in tissue. An experimental method that produces a multifrequency radiation force is also presented. Experimental harmonic motion detection of simultaneous multifrequency vibration is demonstrated using a single transducer. This method can accurately detect motion with displacement amplitude as low as 100 to 200 nm in bovine muscle. Vibration phase can be measured within 10° or less. The experimental results validate the conclusions observed from the model and show multifrequency vibration induction and measurements can be performed simultaneously. PMID:18986892

Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling

USGS Publications Warehouse

Van Den Hoek, Jamon; Read, Jordan S.; Winslow, Luke A.; Montesano, Paul; Markfort, Corey D.

2015-01-01

Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, hs, being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived hs was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of hs, respective influences of land cover type and buffer width on hsare examined; and the effect of including satellite-based hs on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 hs uncertainty was comparable to or better than other satellite-based measures of hs, its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating hs compared to other satellite-derived data, but was limited by its consistent underestimation of hs, inability to detect within-buffer hs variability, and differing accuracy across land cover types. Nonetheless, considering a GDEM2 hs-derived wind sheltering potential improved the modeled lake temperature root mean square error for non-forested lakes by 0.72 °C compared to a commonly used wind sheltering model based on lake area alone. While results from this study show promise, the limitations of near-global GDEM2 data in timeliness, temporal and spatial resolution, and vertical accuracy were apparent. As hydrodynamic modeling and high-resolution topographic mapping efforts both expand, future remote sensing-derived vegetation structure data must be improved to meet wind sheltering accuracy requirements to expand our understanding of lake processes.

Waveform LiDAR processing: comparison of classic approaches and optimized Gold deconvolution to characterize vegetation structure and terrain elevation

NASA Astrophysics Data System (ADS)

Zhou, T.; Popescu, S. C.; Krause, K.

2016-12-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: 1) direct decomposition, 2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from discrete LiDAR data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, < 0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, < 1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (< 1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE.

Age-Related Differences in Multiple Task Monitoring

PubMed Central

Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

2014-01-01

Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age. PMID:25215609

Age-related differences in multiple task monitoring.

PubMed

Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

2014-01-01

Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age.

Trace element analysis by EPMA in geosciences: detection limit, precision and accuracy

NASA Astrophysics Data System (ADS)

Batanova, V. G.; Sobolev, A. V.; Magnin, V.

2018-01-01

Use of the electron probe microanalyser (EPMA) for trace element analysis has increased over the last decade, mainly because of improved stability of spectrometers and the electron column when operated at high probe current; development of new large-area crystal monochromators and ultra-high count rate spectrometers; full integration of energy-dispersive / wavelength-dispersive X-ray spectrometry (EDS/WDS) signals; and the development of powerful software packages. For phases that are stable under a dense electron beam, the detection limit and precision can be decreased to the ppm level by using high acceleration voltage and beam current combined with long counting time. Data on 10 elements (Na, Al, P, Ca, Ti, Cr, Mn, Co, Ni, Zn) in olivine obtained on a JEOL JXA-8230 microprobe with tungsten filament show that the detection limit decreases proportionally to the square root of counting time and probe current. For all elements equal or heavier than phosphorus (Z = 15), the detection limit decreases with increasing accelerating voltage. The analytical precision for minor and trace elements analysed in olivine at 25 kV accelerating voltage and 900 nA beam current is 4 - 18 ppm (2 standard deviations of repeated measurements of the olivine reference sample) and is similar to the detection limit of corresponding elements. To analyse trace elements accurately requires careful estimation of background, and consideration of sample damage under the beam and secondary fluorescence from phase boundaries. The development and use of matrix reference samples with well-characterised trace elements of interest is important for monitoring and improving of the accuracy. An evaluation of the accuracy of trace element analyses in olivine has been made by comparing EPMA data for new reference samples with data obtained by different in-situ and bulk analytical methods in six different laboratories worldwide. For all elements, the measured concentrations in the olivine reference sample were found to be identical (within internal precision) to reference values, suggesting that achieved precision and accuracy are similar. The spatial resolution of EPMA in a silicate matrix, even at very extreme conditions (accelerating voltage 25 kV), does not exceed 7 - 8 μm and thus is still better than laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) or secondary ion mass spectrometry (SIMS) of similar precision. These make the electron microprobe an indispensable method with applications in experimental petrology, geochemistry and cosmochemistry.

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

PubMed

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

NASA Astrophysics Data System (ADS)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

The use of echocardiography in Wolff-Parkinson-White syndrome.

PubMed

Cai, Qiangjun; Shuraih, Mossaab; Nagueh, Sherif F

2012-04-01

Endocardial mapping and radiofrequency catheter ablation are well established modalities for the diagnosis and treatment of patients with Wolff-Parkinson-White (WPW) syndrome associated with tachyarrhythmias. However, the electrophysiologic techniques are invasive, require radiation exposure, and lack spatial resolution of cardiac structures. A variety of echocardiographic techniques have been investigated as a non-invasive alternative for accessory pathway localization. Conventional M-mode echocardiography can detect the fine premature wall motion abnormalities associated with WPW syndrome. However, it is unable to identify the exact site of accessory pathway with sufficient accuracy. 2D, 2D-guided M-mode, and 2D phase analysis techniques are limited by image quality and endocardial border definition. Various modalities of tissue Doppler echocardiography significantly increase the accuracy of left-sided accessory pathway localization to 80-90% even in patients with poor acoustic window. However, right-sided pathways remain a diagnostic challenge. Strain echocardiography by speckle tracking has recently been evaluated and appears promising. Different cardiac abnormalities have been detected by echocardiography in WPW patients. Patients with WPW syndrome and tachyarrhythmias have impaired systolic and diastolic function which improves after radiofrequency ablation. Echocardiography is useful in identifying patient with accessory pathway-associated left ventricular dyssynchrony and dysfunction who may benefit from ablation therapy. Transesophageal and intracardiac echocardiography have been used to guide ablation procedure. Ablation-related complications detected by routine echocardiography are infrequent, rarely clinically relevant, and of limited value.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

NASA Astrophysics Data System (ADS)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.

A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

NASA Astrophysics Data System (ADS)

Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M.; Ludwig, Michelle S.; Guerrero, Thomas

2013-05-01

Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.

Application of Geodetic Techniques for Antenna Positioning in a Ground Penetrating Radar Method

NASA Astrophysics Data System (ADS)

Mazurkiewicz, Ewelina; Ortyl, Łukasz; Karczewski, Jerzy

2018-03-01

The accuracy of determining the location of detectable subsurface objects is related to the accuracy of the position of georadar traces in a given profile, which in turn depends on the precise assessment of the distance covered by an antenna. During georadar measurements the distance covered by an antenna can be determined with a variety of methods. Recording traces at fixed time intervals is the simplest of them. A method which allows for more precise location of georadar traces is recording them at fixed distance intervals, which can be performed with the use of distance triggers (such as a measuring wheel or a hip chain). The search for methods eliminating these discrepancies can be based on the measurement of spatial coordinates of georadar traces conducted with the use of modern geodetic techniques for 3-D location. These techniques include above all a GNSS satellite system and electronic tachymeters. Application of the above mentioned methods increases the accuracy of space location of georadar traces. The article presents the results of georadar measurements performed with the use of geodetic techniques in the test area of Mydlniki in Krakow. A satellite receiver Leica system 1200 and a electronic tachymeter Leica 1102 TCRA were integrated with the georadar equipment. The accuracy of locating chosen subsurface structures was compared.

Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography.

PubMed

Hongbo Guo; Xiaowei He; Muhan Liu; Zeyu Zhang; Zhenhua Hu; Jie Tian

2017-06-01

Cerenkov luminescence tomography (CLT) provides a novel technique for 3-D noninvasive detection of radiopharmaceuticals in living subjects. However, because of the severe scattering of Cerenkov light, the reconstruction accuracy and stability of CLT is still unsatisfied. In this paper, a modified weight multispectral CLT (wmCLT) reconstruction strategy was developed which split the Cerenkov radiation spectrum into several sub-spectral bands and weighted the sub-spectral results to obtain the final result. To better evaluate the property of the wmCLT reconstruction strategy in terms of accuracy, stability and practicability, several numerical simulation experiments and in vivo experiments were conducted and the results obtained were compared with the traditional multispectral CLT (mCLT) and hybrid-spectral CLT (hCLT) reconstruction strategies. The numerical simulation results indicated that wmCLT strategy significantly improved the accuracy of Cerenkov source localization and intensity quantitation and exhibited good stability in suppressing noise in numerical simulation experiments. And the comparison of the results achieved from different in vivo experiments further indicated significant improvement of the wmCLT strategy in terms of the shape recovery of the bladder and the spatial resolution of imaging xenograft tumors. Overall the strategy reported here will facilitate the development of nuclear and optical molecular tomography in theoretical study.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

Cumulative detection probabilities and range accuracy of a pulsed Geiger-mode avalanche photodiode laser ranging system

NASA Astrophysics Data System (ADS)

Luo, Hanjun; Ouyang, Zhengbiao; Liu, Qiang; Chen, Zhiliang; Lu, Hualan

2017-10-01

Cumulative pulses detection with appropriate cumulative pulses number and threshold has the ability to improve the detection performance of the pulsed laser ranging system with GM-APD. In this paper, based on Poisson statistics and multi-pulses cumulative process, the cumulative detection probabilities and their influence factors are investigated. With the normalized probability distribution of each time bin, the theoretical model of the range accuracy and precision is established, and the factors limiting the range accuracy and precision are discussed. The results show that the cumulative pulses detection can produce higher target detection probability and lower false alarm probability. However, for a heavy noise level and extremely weak echo intensity, the false alarm suppression performance of the cumulative pulses detection deteriorates quickly. The range accuracy and precision is another important parameter evaluating the detection performance, the echo intensity and pulse width are main influence factors on the range accuracy and precision, and higher range accuracy and precision is acquired with stronger echo intensity and narrower echo pulse width, for 5-ns echo pulse width, when the echo intensity is larger than 10, the range accuracy and precision lower than 7.5 cm can be achieved.

Usefulness of Derived Frank Lead Parameters in Screening for Coronary Artery Disease and Cardiomyopathy

NASA Technical Reports Server (NTRS)

DePalma, J. L.; Schlegel, T. T.; Arenare, B.; Greco, E. C.; Starc, V.; Rahman, M. A.; Delgado, R.

2007-01-01

We investigated the accuracy of several known as well as newly-introduced derived Frank-lead ECG parameters in differentiating healthy individuals from patients with obstructive coronary artery disease (CAD) and cardiomyopathy (CM). Advanced high-fidelity 12-lead ECG tests (approx. 5-min supine) were first performed on a "training set" of 99 individuals: 33 with ischemic or dilated CM and low ejection fraction (EF less than 40%); 33 with catheterization-proven obstructive CAD but normal EF; and 33 age-/gender-matched healthy controls. The following derived Frank lead parameters were studied for their accuracy in detecting CAD and CM: the spatial ventricular gradient (VG), including its beat-to-beat coefficient of variability (VG CV); the spatial mean QRS (SM-QRS) and T-waves (SM-T) and their beat-to-beat coefficients of variability; the spatial ventricular activation time (VAT); the mean and maximum spatial QRS-T angles; and standard late potentials parameters (RMS40, fQRSD and LAS). Several of these parameters were accurate in discriminating between the control group and both diseased groups at p less than 0.0001. For example the fQRSD, VG CV, mean spatial QRS-T angle and VG minus SM-QRS (which is similar to the SM-T) had retrospective areas under the ROC curve of 0.78, 0.78, 0.80, and 0.84 (CAD vs. controls) and 0.93, 0.88, 0.98 and 0.99 (CM vs. controls), respectively. The single most effective parameter in discriminating between the CAD and CM groups was the spatial VAT (44 plus or minus 5.8 vs. 53 plus or minus 9.9 ms, p less than 0.0001), with an area under the ROC curve of 0.80. Since subsequent prospective analyses using new groups of patients and healthy subjects have yielded only slightly less accurate results, we conclude that derived Frank-lead parameters show great promise for potentially contributing to the development of a rapid and inexpensive resting ECG-based screening test for heart disease.

Intrinsic coincident linear polarimetry using stacked organic photovoltaics.

PubMed

Roy, S Gupta; Awartani, O M; Sen, P; O'Connor, B T; Kudenov, M W

2016-06-27

Polarimetry has widespread applications within atmospheric sensing, telecommunications, biomedical imaging, and target detection. Several existing methods of imaging polarimetry trade off the sensor's spatial resolution for polarimetric resolution, and often have some form of spatial registration error. To mitigate these issues, we have developed a system using oriented polymer-based organic photovoltaics (OPVs) that can preferentially absorb linearly polarized light. Additionally, the OPV cells can be made semitransparent, enabling multiple detectors to be cascaded along the same optical axis. Since each device performs a partial polarization measurement of the same incident beam, high temporal resolution is maintained with the potential for inherent spatial registration. In this paper, a Mueller matrix model of the stacked OPV design is provided. Based on this model, a calibration technique is developed and presented. This calibration technique and model are validated with experimental data, taken with a cascaded three cell OPV Stokes polarimeter, capable of measuring incident linear polarization states. Our results indicate polarization measurement error of 1.2% RMS and an average absolute radiometric accuracy of 2.2% for the demonstrated polarimeter.

Interface Design Implications for Recalling the Spatial Configuration of Virtual Auditory Environments

NASA Astrophysics Data System (ADS)

McMullen, Kyla A.

Although the concept of virtual spatial audio has existed for almost twenty-five years, only in the past fifteen years has modern computing technology enabled the real-time processing needed to deliver high-precision spatial audio. Furthermore, the concept of virtually walking through an auditory environment did not exist. The applications of such an interface have numerous potential uses. Spatial audio has the potential to be used in various manners ranging from enhancing sounds delivered in virtual gaming worlds to conveying spatial locations in real-time emergency response systems. To incorporate this technology in real-world systems, various concerns should be addressed. First, to widely incorporate spatial audio into real-world systems, head-related transfer functions (HRTFs) must be inexpensively created for each user. The present study further investigated an HRTF subjective selection procedure previously developed within our research group. Users discriminated auditory cues to subjectively select their preferred HRTF from a publicly available database. Next, the issue of training to find virtual sources was addressed. Listeners participated in a localization training experiment using their selected HRTFs. The training procedure was created from the characterization of successful search strategies in prior auditory search experiments. Search accuracy significantly improved after listeners performed the training procedure. Next, in the investigation of auditory spatial memory, listeners completed three search and recall tasks with differing recall methods. Recall accuracy significantly decreased in tasks that required the storage of sound source configurations in memory. To assess the impacts of practical scenarios, the present work assessed the performance effects of: signal uncertainty, visual augmentation, and different attenuation modeling. Fortunately, source uncertainty did not affect listeners' ability to recall or identify sound sources. The present study also found that the presence of visual reference frames significantly increased recall accuracy. Additionally, the incorporation of drastic attenuation significantly improved environment recall accuracy. Through investigating the aforementioned concerns, the present study made initial footsteps guiding the design of virtual auditory environments that support spatial configuration recall.

Black-backed woodpecker habitat suitability mapping using conifer snag basal area estimated from airborne laser scanning

NASA Astrophysics Data System (ADS)

Casas Planes, Á.; Garcia, M.; Siegel, R.; Koltunov, A.; Ramirez, C.; Ustin, S.

2015-12-01

Occupancy and habitat suitability models for snag-dependent wildlife species are commonly defined as a function of snag basal area. Although critical for predicting or assessing habitat suitability, spatially distributed estimates of snag basal area are not generally available across landscapes at spatial scales relevant for conservation planning. This study evaluates the use of airborne laser scanning (ALS) to 1) identify individual conifer snags and map their basal area across a recently burned forest, and 2) map habitat suitability for a wildlife species known to be dependent on snag basal area, specifically the black-backed woodpecker (Picoides arcticus). This study focuses on the Rim Fire, a megafire that took place in 2013 in the Sierra Nevada Mountains of California, creating large patches of medium- and high-severity burned forest. We use forest inventory plots, single-tree ALS-derived metrics and Gaussian processes classification and regression to identify conifer snags and estimate their stem diameter and basal area. Then, we use the results to map habitat suitability for the black-backed woodpecker using thresholds for conifer basal area from a previously published habitat suitability model. Local maxima detection and watershed segmentation algorithms resulted in 75% detection of trees with stem diameter larger than 30 cm. Snags are identified with an overall accuracy of 91.8 % and conifer snags are identified with an overall accuracy of 84.8 %. Finally, Gaussian process regression reliably estimated stem diameter (R2 = 0.8) using height and crown area. This work provides a fast and efficient methodology to characterize the extent of a burned forest at the tree level and a critical tool for early wildlife assessment in post-fire forest management and biodiversity conservation.

Reference-free spectroscopic determination of fat and protein in milk in the visible and near infrared region below 1000nm using spatially resolved diffuse reflectance fiber probe.

PubMed

Bogomolov, Andrey; Belikova, Valeria; Galyanin, Vladislav; Melenteva, Anastasiia; Meyer, Hans

2017-05-15

New technique of diffuse reflectance spectroscopic analysis of milk fat and total protein content in the visible (Vis) and adjacent near infrared (NIR) region (400-995nm) has been developed and tested. Sample analysis was performed through a probe having eight 200-µm fiber channels forming a linear array. One of the end fibers was used for the illumination and other seven - for the spectroscopic detection of diffusely reflected light. One of the detection channels was used as a reference to normalize the spectra and to convert them into absorbance-equivalent units. The method has been tested experimentally using a designed sample set prepared from industrial raw milk standards with widely varying fat and protein content. To increase the modelling robustness all milk samples were measured in three different homogenization degrees. Comprehensive data analysis has shown the advantage of combining both spectral and spatial resolution in the same measurement and revealed the most relevant channels and wavelength regions. The modelling accuracy was further improved using joint variable selection and preprocessing optimization method based on the genetic algorithm. The root mean-square errors of different validation methods were below 0.10% for fat and below 0.08% for total protein content. Based on the present experimental data, it was computationally shown that the full-spectrum analysis in this method can be replaced by a sensor measurement at several specific wavelengths, for instance, using light-emitting diodes (LEDs) for illumination. Two optimal sensor configurations have been suggested: with nine LEDs for the analysis of fat and seven - for protein content. Both simulated sensors exhibit nearly the same component determination accuracy as corresponding full-spectrum analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

PubMed

Baldeck, Claire A; Asner, Gregory P; Martin, Robin E; Anderson, Christopher B; Knapp, David E; Kellner, James R; Wright, S Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

SU-E-I-33: Initial Evaluation of Model-Based Iterative CT Reconstruction Using Standard Image Quality Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gingold, E; Dave, J

2014-06-01

Purpose: The purpose of this study was to compare a new model-based iterative reconstruction with existing reconstruction methods (filtered backprojection and basic iterative reconstruction) using quantitative analysis of standard image quality phantom images. Methods: An ACR accreditation phantom (Gammex 464) and a CATPHAN600 phantom were scanned using 3 routine clinical acquisition protocols (adult axial brain, adult abdomen, and pediatric abdomen) on a Philips iCT system. Each scan was acquired using default conditions and 75%, 50% and 25% dose levels. Images were reconstructed using standard filtered backprojection (FBP), conventional iterative reconstruction (iDose4) and a prototype model-based iterative reconstruction (IMR). Phantom measurementsmore » included CT number accuracy, contrast to noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD), and noise power spectrum (NPS). Results: The choice of reconstruction method had no effect on CT number accuracy, or MTF (p<0.01). The CNR of a 6 HU contrast target was improved by 1–67% with iDose4 relative to FBP, while IMR improved CNR by 145–367% across all protocols and dose levels. Within each scan protocol, the CNR improvement from IMR vs FBP showed a general trend of greater improvement at lower dose levels. NPS magnitude was greatest for FBP and lowest for IMR. The NPS of the IMR reconstruction showed a pronounced decrease with increasing spatial frequency, consistent with the unusual noise texture seen in IMR images. Conclusion: Iterative Model Reconstruction reduces noise and improves contrast-to-noise ratio without sacrificing spatial resolution in CT phantom images. This offers the possibility of radiation dose reduction and improved low contrast detectability compared with filtered backprojection or conventional iterative reconstruction.« less

Application of High-Throughput Seebeck Microprobe Measurements on Thermoelectric Half-Heusler Thin Film Combinatorial Material Libraries.

PubMed

Ziolkowski, Pawel; Wambach, Matthias; Ludwig, Alfred; Mueller, Eckhard

2018-01-08

In view of the variety and complexity of thermoelectric (TE) material systems, combinatorial approaches to materials development come to the fore for identifying new promising compounds. The success of this approach is related to the availability and reliability of high-throughput characterization methods for identifying interrelations between materials structures and properties within the composition spread libraries. A meaningful characterization starts with determination of the Seebeck coefficient as a major feature of TE materials. Its measurement, and hence the accuracy and detectability of promising material compositions, may be strongly affected by thermal and electrical measurement conditions. This work illustrates the interrelated effects of the substrate material, the layer thickness, and spatial property distributions of thin film composition spread libraries, which are studied experimentally by local thermopower scans by means of the Potential and Seebeck Microprobe (PSM). The study is complemented by numerical evaluation. Material libraries of the half-Heusler compound system Ti-Ni-Sn were deposited on selected substrates (Si, AlN, Al 2 O 3 ) by magnetron sputtering. Assuming homogeneous properties of a film, significant decrease of the detected thermopower S m can be expected on substrates with higher thermal conductivity, yielding an underestimation of materials thermopower between 15% and 50%, according to FEM (finite element methods) simulations. Thermally poor conducting substrates provide a better accuracy with thermopower underestimates lower than 8%, but suffer from a lower spatial resolution. According to FEM simulations, local scanning of sharp thermopower peaks on lowly conductive substrates is linked to an additional deviation of the measured thermopower of up to 70% compared to homogeneous films, which is 66% higher than for corresponding cases on substrates with higher thermal conductivity of this study.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

PubMed Central

Baldeck, Claire A.; Asner, Gregory P.; Martin, Robin E.; Anderson, Christopher B.; Knapp, David E.; Kellner, James R.; Wright, S. Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. PMID:26153693

Validation of satellite-based CI detection of convective storms via backward trajectories

NASA Astrophysics Data System (ADS)

Dietzsch, Felix; Senf, Fabian; Deneke, Hartwig

2013-04-01

Within this study, the rapid development and evolution of several severe convective events is investigated based on geostationary satellite images, and is related to previous findings on suitable detection thresholds for convective initiation. Nine severe events have been selected that occurred over Central Europe in summer 2012, and have been classified into the categories supercell, mesoscale convective system, frontal system and orographic convection. The cases are traced backward starting from the fully developed convective systems to its very beginning initial state using ECMWF data with 0.5 degree spatial resolution and 3h temporal resolution. For every case the storm life cycle was quantified through the storm's infrared (IR) brightness temperatures obtained from Meteosat Second Generation SEVIRI with 5 min temporal resolution and 4.5 km spatial resolution. In addition, cloud products including cloud optical thickness, cloud phase and effective droplet radius have been taken into account. A semi-automatic adjustment of the tracks within a search box was necessary to improve the tracking accuracy and thus the quality of the derived life-cycles. The combination of IR brightness temperatures, IR temperature time trends and satellite-based cloud products revealed different stages of storm development such as updraft intensification and glaciation well in most casesconfirming previously developed CI criteria from other studies. The vertical temperature gradient between 850 and 500 hPa, the Total-Totals-Index and the storm-relative helicity have been derived from ECMWF data and were used to characterize the storm synoptic environment. The results suggest that the storm-relative helicity also influences the life time of convective storms over Central Europe confirming previous studies. Tracking accuracy has shown to be a crucial issue in our study and a fully automated approach is required to enlarge the number of cases for significant statistics.

Alien Plant Monitoring with Ultralight Airborne Imaging Spectroscopy

PubMed Central

Calviño-Cancela, María; Méndez-Rial, Roi; Reguera-Salgado, Javier; Martín-Herrero, Julio

2014-01-01

Effective management of invasive plants requires a precise determination of their distribution. Remote sensing techniques constitute a promising alternative to field surveys and hyperspectral sensors (also known as imaging spectrometers, with a large number of spectral bands and high spectral resolution) are especially suitable when very similar categories are to be distinguished (e.g. plant species). A main priority in the development of this technology is to lower its cost and simplify its use, so that its demonstrated aptitude for many environmental applications can be truly realized. With this aim, we have developed a system for hyperspectral imaging (200 spectral bands in the 380–1000 nm range and circa 3 nm spectral resolution) operated on board ultralight aircraft (namely a gyrocopter), which allows a drastic reduction of the running costs and operational complexity of image acquisition, and also increases the spatial resolution of the images (circa 5–8 pixels/m2 at circa 65 km/h and 300 m height). The detection system proved useful for the species tested (Acacia melanoxylon, Oxalis pes-caprae, and Carpobrotus aff. edulis and acinaciformis), with user’s and producer’s accuracy always exceeding 90%. The detection accuracy reported corresponds to patches down to 0.125 m2 (50% of pixels 0.5×0.5 m in size), a very small size for many plant species, making it very effective for initial stages of invasive plant spread. In addition, its low operating costs, similar to those of a 4WD ground vehicle, facilitate frequent image acquisition. Acquired images constitute a permanent record of the status of the study area, with great amount of information that can be analyzed in the future for other purposes, thus greatly facilitating the monitoring of natural areas at detailed spatial and temporal scales for improved management. PMID:25010601

Automated determination of arterial input function for DCE-MRI of the prostate

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Chang, Ming-Ching; Gupta, Sandeep

2011-03-01

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.

An optical sensor for detecting the contact location of a gas-liquid interface on a body.

PubMed

Belden, Jesse; Jandron, Michael

2014-08-01

An optical sensor for detecting the dynamic contact location of a gas-liquid interface along the length of a body is described. The sensor is developed in the context of applications to supercavitating bodies requiring measurement of the dynamic cavity contact location; however, the sensing method is extendable to other applications as well. The optical principle of total internal reflection is exploited to detect changes in refractive index of the medium contacting the body at discrete locations along its length. The derived theoretical operation of the sensor predicts a signal attenuation of 18 dB when a sensed location changes from air-contacting to water-contacting. Theory also shows that spatial resolution (d) scales linearly with sensor length (L(s)) and a resolution of 0.01L(s) can be achieved. A prototype sensor is constructed from simple components and response characteristics are quantified for different ambient light conditions as well as partial wetting states. Three methods of sensor calibration are described and a signal processing framework is developed that allows for robust detection of the gas-liquid contact location. In a tank draining experiment, the prototype sensor resolves the water level with accuracy limited only by the spatial resolution, which is constrained by the experimental setup. A more representative experiment is performed in which the prototype sensor accurately measures the dynamic contact location of a gas cavity on a water tunnel wall.

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

2014-01-01

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

qCSF in Clinical Application: Efficient Characterization and Classification of Contrast Sensitivity Functions in Amblyopia

PubMed Central

Hou, Fang; Huang, Chang-Bing; Lesmes, Luis; Feng, Li-Xia; Tao, Liming; Zhou, Yi-Feng; Lu, Zhong-Lin

2010-01-01

Purpose. The qCSF method is a novel procedure for rapid measurement of spatial contrast sensitivity functions (CSFs). It combines Bayesian adaptive inference with a trial-to-trial information gain strategy, to directly estimate four parameters defining the observer's CSF. In the present study, the suitability of the qCSF method for clinical application was examined. Methods. The qCSF method was applied to rapidly assess spatial CSFs in 10 normal and 8 amblyopic participants. The qCSF was evaluated for accuracy, precision, test–retest reliability, suitability of CSF model assumptions, and accuracy of amblyopia screening. Results. qCSF estimates obtained with as few as 50 trials matched those obtained with 300 Ψ trials. The precision of qCSF estimates obtained with 120 and 130 trials, in normal subjects and amblyopes, matched the precision of 300 Ψ trials. For both groups and both methods, test–retest sensitivity estimates were well matched (all R > 0.94). The qCSF model assumptions were valid for 8 of 10 normal participants and all amblyopic participants. Measures of the area under log CSF (AULCSF) and the cutoff spatial frequency (cutSF) were lower in the amblyopia group; these differences were captured within 50 qCSF trials. Amblyopia was detected at an approximately 80% correct rate in 50 trials, when a logistic regression model was used with AULCSF and cutSF as predictors. Conclusions. The qCSF method is sufficiently rapid, accurate, and precise in measuring CSFs in normal and amblyopic persons. It has great potential for clinical practice. PMID:20484592

Increasing Accuracy in Computed Inviscid Boundary Conditions

NASA Technical Reports Server (NTRS)

Dyson, Roger

2004-01-01

A technique has been devised to increase the accuracy of computational simulations of flows of inviscid fluids by increasing the accuracy with which surface boundary conditions are represented. This technique is expected to be especially beneficial for computational aeroacoustics, wherein it enables proper accounting, not only for acoustic waves, but also for vorticity and entropy waves, at surfaces. Heretofore, inviscid nonlinear surface boundary conditions have been limited to third-order accuracy in time for stationary surfaces and to first-order accuracy in time for moving surfaces. For steady-state calculations, it may be possible to achieve higher accuracy in space, but high accuracy in time is needed for efficient simulation of multiscale unsteady flow phenomena. The present technique is the first surface treatment that provides the needed high accuracy through proper accounting of higher-order time derivatives. The present technique is founded on a method known in art as the Hermitian modified solution approximation (MESA) scheme. This is because high time accuracy at a surface depends upon, among other things, correction of the spatial cross-derivatives of flow variables, and many of these cross-derivatives are included explicitly on the computational grid in the MESA scheme. (Alternatively, a related method other than the MESA scheme could be used, as long as the method involves consistent application of the effects of the cross-derivatives.) While the mathematical derivation of the present technique is too lengthy and complex to fit within the space available for this article, the technique itself can be characterized in relatively simple terms: The technique involves correction of surface-normal spatial pressure derivatives at a boundary surface to satisfy the governing equations and the boundary conditions and thereby achieve arbitrarily high orders of time accuracy in special cases. The boundary conditions can now include a potentially infinite number of time derivatives of surface-normal velocity (consistent with no flow through the boundary) up to arbitrarily high order. The corrections for the first-order spatial derivatives of pressure are calculated by use of the first-order time derivative velocity. The corrected first-order spatial derivatives are used to calculate the second- order time derivatives of velocity, which, in turn, are used to calculate the corrections for the second-order pressure derivatives. The process as described is repeated, progressing through increasing orders of derivatives, until the desired accuracy is attained.

ON THE DETECTION OF GLOBAL 21-cm SIGNAL FROM REIONIZATION USING INTERFEROMETERS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Saurabh; Subrahmanyan, Ravi; Shankar, N. Udaya

2015-12-20

Detection of the global redshifted 21-cm signal is an excellent means of deciphering the physical processes during the Dark Ages and subsequent Epoch of Reionization (EoR). However, detection of this faint monopole is challenging due to the high precision required in instrumental calibration and modeling of substantially brighter foregrounds and instrumental systematics. In particular, modeling of receiver noise with mK accuracy and its separation remains a formidable task in experiments aiming to detect the global signal using single-element spectral radiometers. Interferometers do not respond to receiver noise; therefore, here we explore the theory of the response of interferometers to globalmore » signals. In other words, we discuss the spatial coherence in the electric field arising from the monopole component of the 21-cm signal and methods for its detection using sensor arrays. We proceed by first deriving the response to uniform sky of two-element interferometers made of unit dipole and resonant loop antennas, then extend the analysis to interferometers made of one-dimensional arrays and also consider two-dimensional aperture antennas. Finally, we describe methods by which the coherence might be enhanced so that the interferometer measurements yield improved sensitivity to the monopole component. We conclude (a) that it is indeed possible to measure the global 21-cm from EoR using interferometers, (b) that a practically useful configuration is with omnidirectional antennas as interferometer elements, and (c) that the spatial coherence may be enhanced using, for example, a space beam splitter between the interferometer elements.« less

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

USGS Publications Warehouse

Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

2003-01-01

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment

NASA Astrophysics Data System (ADS)

Rasel, Sikdar M. M.; Chang, Hsing-Chung; Diti, Israt Jahan; Ralph, Tim; Saintilan, Neil

2016-05-01

Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper.

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

DOE PAGES

Wang, Wei; Chen, Jiayu; Hong, Tianzhen

2018-04-07

We present that occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants’ spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolutionmore » occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. Finally, the results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.« less

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Wei; Chen, Jiayu; Hong, Tianzhen

We present that occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants’ spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolutionmore » occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. Finally, the results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.« less

Spatial and temporal processing in healthy aging: implications for perceptions of driving skills.

PubMed

Conlon, Elizabeth; Herkes, Kathleen

2008-07-01

Sensitivity to the attributes of a stimulus (form or motion) and accuracy when detecting rapidly presented stimulus information were measured in older (N = 36) and younger (N = 37) groups. Before and after practice, the older group was significantly less sensitive to global motion (but not to form) and less accurate on a rapid sequencing task when detecting the individual elements presented in long but not short sequences. These effect sizes produced power for the different analyses that ranged between 0.5 and 1.00. The reduced sensitivity found among older individuals to temporal but not spatial stimuli, adds support to previous findings of a selective age-related deficit in temporal processing. Older women were significantly less sensitive than older men, younger men and younger women on the global motion task. Gender effects were evident when, in response to global motion stimuli, complex extraction and integration processes needed to be undertaken rapidly. Significant moderate correlations were found between age, global motion sensitivity and reports of perceptions of other vehicles and road signs when driving. These associations suggest that reduced motion sensitivity may produce functional difficulties for the older adults when judging speeds or estimating gaps in traffic while driving.

Remote sensing of landscape-level coastal environmental indicators.

PubMed

Klemas, V V

2001-01-01

Advances in technology and decreases in cost are making remote sensing (RS) and geographic information systems (GIS) practical and attractive for use in coastal resource management. They are also allowing researchers and managers to take a broader view of ecological patterns and processes. Landscape-level environmental indicators that can be detected by Landsat Thematic Mapper (TM) and other remote sensors are available to provide quantitative estimates of coastal and estuarine habitat conditions and trends. Such indicators include watershed land cover, riparian buffers, shoreline and wetland changes, among others. With the launch of Landsat 7, the cost of TM imagery has dropped by nearly a factor of 10, decreasing the cost of monitoring large coastal areas and estuaries. New satellites, carrying sensors with much finer spatial (1-5 m) and spectral (200 narrow bands) resolutions are being launched, providing a capability to more accurately detect changes in coastal habitat and wetland health. Advances in the application of GIS help incorporate ancillary data layers to improve the accuracy of satellite land-cover classification. When these techniques for generating, organizing, storing, and analyzing spatial information are combined with mathematical models, coastal planners and managers have a means for assessing the impacts of alternative management practices.

Testing the quality of images for permanent magnet desktop MRI systems using specially designed phantoms.

PubMed

Qiu, Jianfeng; Wang, Guozhu; Min, Jiao; Wang, Xiaoyan; Wang, Pengcheng

2013-12-21

Our aim was to measure the performance of desktop magnetic resonance imaging (MRI) systems using specially designed phantoms, by testing imaging parameters and analysing the imaging quality. We designed multifunction phantoms with diameters of 18 and 60 mm for desktop MRI scanners in accordance with the American Association of Physicists in Medicine (AAPM) report no. 28. We scanned the phantoms with three permanent magnet 0.5 T desktop MRI systems, measured the MRI image parameters, and analysed imaging quality by comparing the data with the AAPM criteria and Chinese national standards. Image parameters included: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, signal-to-noise ratio (SNR), and image uniformity. The image parameters of three desktop MRI machines could be measured using our specially designed phantoms, and most parameters were in line with MRI quality control criterion, including: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, image uniformity and slice position accuracy. However, SNR was significantly lower than in some references. The imaging test and quality control are necessary for desktop MRI systems, and should be performed with the applicable phantom and corresponding standards.

Huperzine A: Behavioral and Pharmacological Evaluation in Rhesus Monkeys

DTIC Science & Technology

2008-06-01

challenged with 30 ug/kg scopolamine . Doses of 1 and 10 ug/kg HUP improved choice accuracy on a previously learned delayed spatial memory task in the...elderly subjects, and doses of 10 and 100 ug/kg reversed the scopolamine -induced deficits in the younger monkeys. Unfortunately, no data regarding...interval) in the spatial memory task differentially modulated the drug effects on performance. Specifically, scopolamine impaired accuracy

Spatial localization deficits and auditory cortical dysfunction in schizophrenia

PubMed Central

Perrin, Megan A.; Butler, Pamela D.; DiCostanzo, Joanna; Forchelli, Gina; Silipo, Gail; Javitt, Daniel C.

2014-01-01

Background Schizophrenia is associated with deficits in the ability to discriminate auditory features such as pitch and duration that localize to primary cortical regions. Lesions of primary vs. secondary auditory cortex also produce differentiable effects on ability to localize and discriminate free-field sound, with primary cortical lesions affecting variability as well as accuracy of response. Variability of sound localization has not previously been studied in schizophrenia. Methods The study compared performance between patients with schizophrenia (n=21) and healthy controls (n=20) on sound localization and spatial discrimination tasks using low frequency tones generated from seven speakers concavely arranged with 30 degrees separation. Results For the sound localization task, patients showed reduced accuracy (p=0.004) and greater overall response variability (p=0.032), particularly in the right hemifield. Performance was also impaired on the spatial discrimination task (p=0.018). On both tasks, poorer accuracy in the right hemifield was associated with greater cognitive symptom severity. Better accuracy in the left hemifield was associated with greater hallucination severity on the sound localization task (p=0.026), but no significant association was found for the spatial discrimination task. Conclusion Patients show impairments in both sound localization and spatial discrimination of sounds presented free-field, with a pattern comparable to that of individuals with right superior temporal lobe lesions that include primary auditory cortex (Heschl’s gyrus). Right primary auditory cortex dysfunction may protect against hallucinations by influencing laterality of functioning. PMID:20619608

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

Limits of Spatial Attention in Three-Dimensional Space and Dual-task Driving Performance

PubMed Central

Andersen, George J.; Ni, Rui; Bian, Zheng; Kang, Julie

2010-01-01

The present study examined the limits of spatial attention while performing two driving relevant tasks that varied in depth. The first task was to maintain a fixed headway distance behind a lead vehicle that varied speed. The second task was to detect a light-change target in an array of lights located above the roadway. In Experiment 1 the light detection task required drivers to encode color and location. The results indicated that reaction time to detect a light-change target increased and accuracy decreased as a function of the horizontal location of the light-change target and as a function of the distance from the driver. In a second experiment the light change task was changed to a singleton search (detect the onset of a yellow light) and the workload of the car following task was systematically varied. The results of Experiment 2 indicated that RT increased as a function of task workload, the 2D position of the light-change target and the distance of the light-change target. A multiple regression analysis indicated that the effect of distance on light detection performance was not due to changes in the projected size of the light target. In Experiment 3 we found that the distance effect in detecting a light change could not be explained by the location of eye fixations. The results demonstrate that when drivers attend to a roadway scene attention is limited in three-dimensional space. These results have important implications for developing tests for assessing crash risk among drivers as well as the design of in vehicle technologies such as head-up displays. PMID:21094336

Modeling Extra-Long Tsunami Propagation: Assessing Data, Model Accuracy and Forecast Implications

NASA Astrophysics Data System (ADS)

Titov, V. V.; Moore, C. W.; Rabinovich, A.

2017-12-01

Detecting and modeling tsunamis propagating tens of thousands of kilometers from the source is a formidable scientific challenge and seemingly satisfies only scientific curiosity. However, results of such analyses produce a valuable insight into the tsunami propagation dynamics, model accuracy and would provide important implications for tsunami forecast. The Mw = 9.3 megathrust earthquake of December 26, 2004 off the coast of Sumatra generated a tsunami that devastated Indian Ocean coastlines and spread into the Pacific and Atlantic oceans. The tsunami was recorded by a great number of coastal tide gauges, including those located in 15-25 thousand kilometers from the source area. To date, it is still the farthest instrumentally detected tsunami. The data from these instruments throughout the world oceans enabled to estimate various statistical parameters and energy decay of this event. High-resolution records of this tsunami from DARTs 32401 (offshore of northern Chile), 46405 and NeMO (both offshore of the US West Coast), combined with the mainland tide gauge measurements enabled us to examine far-field characteristics of the 2004 in the Pacific Ocean and to compare the results of global numerical simulations with the observations. Despite their small heights (less than 2 cm at deep-ocean locations), the records demonstrated consistent spatial and temporal structure. The numerical model described well the frequency content, amplitudes and general structure of the observed waves at deep-ocean and coastal gages. We present analysis of the measurements and comparison with model data to discuss implication for tsunami forecast accuracy. Model study for such extreme distances from the tsunami source and at extra-long times after the event is an attempt to find accuracy bounds for tsunami models and accuracy limitations of model use for forecast. We discuss results in application to tsunami model forecast and tsunami modeling in general.

Photoacoustic microscopy of bilirubin in tissue phantoms

NASA Astrophysics Data System (ADS)

Zhou, Yong; Zhang, Chi; Yao, Da-Kang; Wang, Lihong V.

2012-12-01

Determining both bilirubin's concentration and its spatial distribution are important in disease diagnosis. Here, for the first time, we applied quantitative multiwavelength photoacoustic microscopy (PAM) to detect bilirubin concentration and distribution simultaneously. By measuring tissue-mimicking phantoms with different bilirubin concentrations, we showed that the root-mean-square error of prediction has reached 0.52 and 0.83 mg/dL for pure bilirubin and for blood-mixed bilirubin detection (with 100% oxygen saturation), respectively. We further demonstrated the capability of the PAM system to image bilirubin distribution both with and without blood. Finally, by underlaying bilirubin phantoms with mouse skins, we showed that bilirubin can be imaged with consistent accuracy down to >400 μm in depth. Our results show that PAM has potential for noninvasive bilirubin monitoring in vivo, as well as for further clinical applications.

The Geopotential Research Mission - Mapping the near earth gravity and magnetic fields

NASA Technical Reports Server (NTRS)

Taylor, P. T.; Keating, T.; Smith, D. E.; Langel, R. A.; Schnetzler, C. C.; Kahn, W. D.

1983-01-01

The Geopotential Research Mission (GRM), NASA's low-level satellite system designed to measure the gravity and magnetic fields of the earth, and its objectives are described. The GRM will consist of two, Shuttle launched, satellite systems (300 km apart) that will operate simultaneously at a 160 km circular-polar orbit for six months. Current mission goals include mapping the global geoid to 10 cm, measuring gravity-field anomalies to 2 mgal with a spatial resolution of 100 km, detecting crustal magnetic anomalies of 100 km wavelength with 1 nT accuracy, measuring the vectors components to + or - 5 arc sec and 5 nT, and computing the main dipole or core field to 5 nT with a 2 nT/year secular variation detection. Resource analysis and exploration geology are additional applications considered.

Design of an FMCW radar baseband signal processing system for automotive application.

PubMed

Lin, Jau-Jr; Li, Yuan-Ping; Hsu, Wei-Chiang; Lee, Ta-Sung

2016-01-01

For a typical FMCW automotive radar system, a new design of baseband signal processing architecture and algorithms is proposed to overcome the ghost targets and overlapping problems in the multi-target detection scenario. To satisfy the short measurement time constraint without increasing the RF front-end loading, a three-segment waveform with different slopes is utilized. By introducing a new pairing mechanism and a spatial filter design algorithm, the proposed detection architecture not only provides high accuracy and reliability, but also requires low pairing time and computational loading. This proposed baseband signal processing architecture and algorithms balance the performance and complexity, and are suitable to be implemented in a real automotive radar system. Field measurement results demonstrate that the proposed automotive radar signal processing system can perform well in a realistic application scenario.

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

PubMed Central

Zhou, Yangbo; Fox, Daniel S; Maguire, Pierce; O’Connell, Robert; Masters, Robert; Rodenburg, Cornelia; Wu, Hanchun; Dapor, Maurizio; Chen, Ying; Zhang, Hongzhou

2016-01-01

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10 meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy. PMID:26878907

[Identification of Systemic Contaminations with Legionella Spec. in Drinking Water Plumbing Systems: Sampling Strategies and Corresponding Parameters].

PubMed

Völker, S; Schreiber, C; Müller, H; Zacharias, N; Kistemann, T

2017-05-01

After the amendment of the Drinking Water Ordinance in 2011, the requirements for the hygienic-microbiological monitoring of drinking water installations have increased significantly. In the BMBF-funded project "Biofilm Management" (2010-2014), we examined the extent to which established sampling strategies in practice can uncover drinking water plumbing systems systemically colonized with Legionella. Moreover, we investigated additional parameters that might be suitable for detecting systemic contaminations. We subjected the drinking water plumbing systems of 8 buildings with known microbial contamination (Legionella) to an intensive hygienic-microbiological sampling with high spatial and temporal resolution. A total of 626 drinking hot water samples were analyzed with classical culture-based methods. In addition, comprehensive hygienic observations were conducted in each building and qualitative interviews with operators and users were applied. Collected tap-specific parameters were quantitatively analyzed by means of sensitivity and accuracy calculations. The systemic presence of Legionella in drinking water plumbing systems has a high spatial and temporal variability. Established sampling strategies were only partially suitable to detect long-term Legionella contaminations in practice. In particular, the sampling of hot water at the calorifier and circulation re-entrance showed little significance in terms of contamination events. To detect the systemic presence of Legionella,the parameters stagnation (qualitatively assessed) and temperature (compliance with the 5K-rule) showed better results. © Georg Thieme Verlag KG Stuttgart · New York.

Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Wang, Shifeng; So, Emily; Smith, Pete

2015-04-01

Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

PubMed Central

Swetnam, Tyson L.; Gillan, Jeffrey K.; Sankey, Temuulen T.; McClaran, Mitchel P.; Nichols, Mary H.; Heilman, Philip; McVay, Jason

2018-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. PMID:29379511

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States.

PubMed

Swetnam, Tyson L; Gillan, Jeffrey K; Sankey, Temuulen T; McClaran, Mitchel P; Nichols, Mary H; Heilman, Philip; McVay, Jason

2017-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.

High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices.

PubMed

Savari, Maryam; Abdul Wahab, Ainuddin Wahid; Anuar, Nor Badrul

2016-09-01

Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

k-t SENSE-accelerated Myocardial Perfusion MR Imaging at 3.0 Tesla - comparison with 1.5 Tesla

PubMed Central

Plein, Sven; Schwitter, Juerg; Suerder, Daniel; Greenwood, John P.; Boesiger, Peter; Kozerke, Sebastian

2008-01-01

Purpose To determine the feasibility and diagnostic accuracy of high spatial resolution myocardial perfusion MR at 3.0 Tesla using k-space and time domain undersampling with sensitivity encoding (k-t SENSE). Materials and Methods The study was reviewed and approved by the local ethic review board. k-t SENSE perfusion MR was performed at 1.5 Tesla and 3.0 Tesla (saturation recovery gradient echo pulse sequence, repetition time/echo time 3.0ms/1.0ms, flip angle 15°, 5x k-t SENSE acceleration, spatial resolution 1.3×1.3×10mm3). Fourteen volunteers were studied at rest and 37 patients during adenosine stress. In volunteers, comparison was also made with standard-resolution (2.5×2.5×10mm3) 2x SENSE perfusion MR at 3.0 Tesla. Image quality, artifact scores, signal-to-noise ratios (SNR) and contrast-enhancement ratios (CER) were derived. In patients, diagnostic accuracy of visual analysis to detect >50% diameter stenosis on quantitative coronary angiography was determined by receiver-operator-characteristics (ROC). Results In volunteers, image quality and artifact scores were similar for 3.0 Tesla and 1.5 Tesla, while SNR was higher (11.6 vs. 5.6) and CER lower (1.1 vs. 1.5, p=0.012) at 3.0 Tesla. Compared with standard-resolution perfusion MR, image quality was higher for k-t SENSE (3.6 vs. 3.1, p=0.04), endocardial dark rim artifacts were reduced (artifact thickness 1.6mm vs. 2.4mm, p<0.001) and CER similar. In patients, area under the ROC curve for detection of coronary stenosis was 0.89 and 0.80, p=0.21 for 3.0 Tesla and 1.5 Tesla, respectively. Conclusions k-t SENSE accelerated high-resolution perfusion MR at 3.0 Tesla is feasible with similar artifacts and diagnostic accuracy as at 1.5 Tesla. Compared with standard-resolution perfusion MR, image quality is improved and artifacts are reduced. PMID:18936311

Spatially distributed modeling of soil organic carbon across China with improved accuracy

NASA Astrophysics Data System (ADS)

Li, Qi-quan; Zhang, Hao; Jiang, Xin-ye; Luo, Youlin; Wang, Chang-quan; Yue, Tian-xiang; Li, Bing; Gao, Xue-song

2017-06-01

There is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014.

PubMed

Ma, Jun; Xiao, Xiangming; Zhang, Yao; Doughty, Russell; Chen, Bangqian; Zhao, Bin

2018-10-15

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP VPM ) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP VPM is also significantly positive correlated with GOME-2 SIF (R 2  > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models. Copyright © 2018 Elsevier B.V. All rights reserved.

An introductory analysis of digital infrared thermal imaging guided oral cancer detection using multiresolution rotation invariant texture features

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Das Gupta, R.; Mukhopadhyay, S.; Anjum, N.; Patsa, S.; Ray, J. G.

2017-03-01

This manuscript presents an analytical treatment on the feasibility of multi-scale Gabor filter bank response for non-invasive oral cancer pre-screening and detection in the long infrared spectrum. Incapability of present healthcare technology to detect oral cancer in budding stage manifests in high mortality rate. The paper contributes a step towards automation in non-invasive computer-aided oral cancer detection using an amalgamation of image processing and machine intelligence paradigms. Previous works have shown the discriminative difference of facial temperature distribution between a normal subject and a patient. The proposed work, for the first time, exploits this difference further by representing the facial Region of Interest(ROI) using multiscale rotation invariant Gabor filter bank responses followed by classification using Radial Basis Function(RBF) kernelized Support Vector Machine(SVM). The proposed study reveals an initial increase in classification accuracy with incrementing image scales followed by degradation of performance; an indication that addition of more and more finer scales tend to embed noisy information instead of discriminative texture patterns. Moreover, the performance is consistently better for filter responses from profile faces compared to frontal faces.This is primarily attributed to the ineptness of Gabor kernels to analyze low spatial frequency components over a small facial surface area. On our dataset comprising of 81 malignant, 59 pre-cancerous, and 63 normal subjects, we achieve state-of-the-art accuracy of 85.16% for normal v/s precancerous and 84.72% for normal v/s malignant classification. This sets a benchmark for further investigation of multiscale feature extraction paradigms in IR spectrum for oral cancer detection.

Indoor Spatial Updating With Impaired Vision

PubMed Central

Legge, Gordon E.; Granquist, Christina; Baek, Yihwa; Gage, Rachel

2016-01-01

Purpose Spatial updating is the ability to keep track of position and orientation while moving through an environment. We asked how normally sighted and visually impaired subjects compare in spatial updating and in estimating room dimensions. Methods Groups of 32 normally sighted, 16 low-vision, and 16 blind subjects estimated the dimensions of six rectangular rooms. Updating was assessed by guiding the subjects along three-segment paths in the rooms. At the end of each path, they estimated the distance and direction to the starting location, and to a designated target. Spatial updating was tested in five conditions ranging from free viewing to full auditory and visual deprivation. Results The normally sighted and low-vision groups did not differ in their accuracy for judging room dimensions. Correlations between estimated size and physical size were high. Accuracy of low-vision performance was not correlated with acuity, contrast sensitivity, or field status. Accuracy was lower for the blind subjects. The three groups were very similar in spatial-updating performance, and exhibited only weak dependence on the nature of the viewing conditions. Conclusions People with a wide range of low-vision conditions are able to judge room dimensions as accurately as people with normal vision. Blind subjects have difficulty in judging the dimensions of quiet rooms, but some information is available from echolocation. Vision status has little impact on performance in simple spatial updating; proprioceptive and vestibular cues are sufficient. PMID:27978556

Indoor Spatial Updating With Impaired Vision.

PubMed

Legge, Gordon E; Granquist, Christina; Baek, Yihwa; Gage, Rachel

2016-12-01

Spatial updating is the ability to keep track of position and orientation while moving through an environment. We asked how normally sighted and visually impaired subjects compare in spatial updating and in estimating room dimensions. Groups of 32 normally sighted, 16 low-vision, and 16 blind subjects estimated the dimensions of six rectangular rooms. Updating was assessed by guiding the subjects along three-segment paths in the rooms. At the end of each path, they estimated the distance and direction to the starting location, and to a designated target. Spatial updating was tested in five conditions ranging from free viewing to full auditory and visual deprivation. The normally sighted and low-vision groups did not differ in their accuracy for judging room dimensions. Correlations between estimated size and physical size were high. Accuracy of low-vision performance was not correlated with acuity, contrast sensitivity, or field status. Accuracy was lower for the blind subjects. The three groups were very similar in spatial-updating performance, and exhibited only weak dependence on the nature of the viewing conditions. People with a wide range of low-vision conditions are able to judge room dimensions as accurately as people with normal vision. Blind subjects have difficulty in judging the dimensions of quiet rooms, but some information is available from echolocation. Vision status has little impact on performance in simple spatial updating; proprioceptive and vestibular cues are sufficient.

End-to-end system of license plate localization and recognition

NASA Astrophysics Data System (ADS)

Zhu, Siyu; Dianat, Sohail; Mestha, Lalit K.

2015-03-01

An end-to-end license plate recognition system is proposed. It is composed of preprocessing, detection, segmentation, and character recognition to find and recognize plates from camera-based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and to recall values for detection. Floating peak and valleys of projection profiles are used to cut the license plates into individual characters. A turning function-based method is proposed to quickly and accurately recognize each character. It is further accelerated using curvature histogram-based support vector machine. The INFTY dataset is used to train the recognition system, and MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems.

High operation temperature of HgCdTe photodiodes by bulk defect passivation

NASA Astrophysics Data System (ADS)

Boieriu, Paul; Velicu, S.; Bommena, R.; Buurma, C.; Blisset, C.; Grein, C.; Sivananthan, S.; Hagler, P.

2013-01-01

Spatial noise and the loss of photogenerated current due material non-uniformities limit the performance of long wavelength infrared (LWIR) HgCdTe detector arrays. Reducing the electrical activity of defects is equivalent to lowering their density, thereby allowing detection and discrimination over longer ranges. Infrared focal plane arrays (IRFPAs) in other spectral bands will also benefit from detectivity and uniformity improvements. Larger signal-to-noise ratios permit either improved accuracy of detection/discrimination when an IRFPA is employed under current operating conditions, or provide similar performance with the IRFPA operating under less stringent conditions such as higher system temperature, increased system jitter or damaged read out integrated circuit (ROIC) wells. The bulk passivation of semiconductors with hydrogen continues to be investigated for its potential to become a tool for the fabrication of high performance devices. Inductively coupled plasmas have been shown to improve the quality and uniformity of semiconductor materials and devices. The retention of the benefits following various aging conditions is discussed here.

Comparison between different cost devices for digital capture of X-ray films: an image characteristics detection approach.

PubMed

Salazar, Antonio José; Camacho, Juan Camilo; Aguirre, Diego Andrés

2012-02-01

A common teleradiology practice is digitizing films. The costs of specialized digitizers are very high, that is why there is a trend to use conventional scanners and digital cameras. Statistical clinical studies are required to determine the accuracy of these devices, which are very difficult to carry out. The purpose of this study was to compare three capture devices in terms of their capacity to detect several image characteristics. Spatial resolution, contrast, gray levels, and geometric deformation were compared for a specialized digitizer ICR (US$ 15,000), a conventional scanner UMAX (US$ 1,800), and a digital camera LUMIX (US$ 450, but require an additional support system and a light box for about US$ 400). Test patterns printed in films were used. The results detected gray levels lower than real values for all three devices; acceptable contrast and low geometric deformation with three devices. All three devices are appropriate solutions, but a digital camera requires more operator training and more settings.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy

PubMed Central

David, Mariana R.

2018-01-01

The accelerating global spread of arboviruses, such as Zika virus (ZIKV), highlights the need for more proactive mosquito surveillance. However, a major challenge during arbovirus outbreaks has been the lack of rapid and affordable tests for pathogen detection in mosquitoes. We show for the first time that near-infrared spectroscopy (NIRS) is a rapid, reagent-free, and cost-effective tool that can be used to noninvasively detect ZIKV in heads and thoraces of intact Aedes aegypti mosquitoes with prediction accuracies of 94.2 to 99.3% relative to quantitative reverse transcription polymerase chain reaction (RT-qPCR). NIRS involves simply shining a beam of light on a mosquito to collect a diagnostic spectrum. We estimated in this study that NIRS is 18 times faster and 110 times cheaper than RT-qPCR. We anticipate that NIRS will be expanded upon for identifying potential arbovirus hotspots to guide the spatial prioritization of vector control. PMID:29806030

Pigmented skin lesion detection using random forest and wavelet-based texture

NASA Astrophysics Data System (ADS)

Hu, Ping; Yang, Tie-jun

2016-10-01

The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

NASA Astrophysics Data System (ADS)

Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

2015-05-01

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

Accuracy Sampling Design Bias on Coarse Spatial Resolution Land Cover Data in the Great Lakes Region (United States and Canada)

EPA Science Inventory

A number of articles have investigated the impact of sampling design on remotely sensed landcover accuracy estimates. Gong and Howarth (1990) found significant differences for Kappa accuracy values when comparing purepixel sampling, stratified random sampling, and stratified sys...

Cross-coherent vector sensor processing for spatially distributed glider networks.

PubMed

Nichols, Brendan; Sabra, Karim G

2015-09-01

Autonomous underwater gliders fitted with vector sensors can be used as a spatially distributed sensor array to passively locate underwater sources. However, to date, the positional accuracy required for robust array processing (especially coherent processing) is not achievable using dead-reckoning while the gliders remain submerged. To obtain such accuracy, the gliders can be temporarily surfaced to allow for global positioning system contact, but the acoustically active sea surface introduces locally additional sensor noise. This letter demonstrates that cross-coherent array processing, which inherently mitigates the effects of local noise, outperforms traditional incoherent processing source localization methods for this spatially distributed vector sensor network.

Entropy of Movement Outcome in Space-Time.

PubMed

Lai, Shih-Chiung; Hsieh, Tsung-Yu; Newell, Karl M

2015-07-01

Information entropy of the joint spatial and temporal (space-time) probability of discrete movement outcome was investigated in two experiments as a function of different movement strategies (space-time, space, and time instructional emphases), task goals (point-aiming and target-aiming) and movement speed-accuracy constraints. The variance of the movement spatial and temporal errors was reduced by instructional emphasis on the respective spatial or temporal dimension, but increased on the other dimension. The space-time entropy was lower in targetaiming task than the point aiming task but did not differ between instructional emphases. However, the joint probabilistic measure of spatial and temporal entropy showed that spatial error is traded for timing error in tasks with space-time criteria and that the pattern of movement error depends on the dimension of the measurement process. The unified entropy measure of movement outcome in space-time reveals a new relation for the speed-accuracy.

Uav-Based Crops Classification with Joint Features from Orthoimage and Dsm Data

NASA Astrophysics Data System (ADS)

Liu, B.; Shi, Y.; Duan, Y.; Wu, W.

2018-04-01

Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.

Performance Evaluation of EnKF-based Hydrogeological Site Characterization using Color Coherent Vectors

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.

2017-12-01

Complexity of hydrogeological systems arises from the multi-scale heterogeneity and insufficient measurements of their underlying parameters such as hydraulic conductivity and porosity. An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of numerical models that predict groundwater flow and solute transport. Therefore, a variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating the governing physical models. In this work, we propose a novel framework for evaluating the performance of these estimation methods. We focus on the Ensemble Kalman Filter (EnKF) approach that is a widely used data assimilation technique. It reconciles multiple sources of measurements to sequentially estimate model parameters such as the hydraulic conductivity. Several methods have been used in the literature to quantify the accuracy of the estimations obtained by EnKF, including Rank Histograms, RMSE and Ensemble Spread. However, these commonly used methods do not regard the spatial information and variability of geological formations. This can cause hydraulic conductivity fields with very different spatial structures to have similar histograms or RMSE. We propose a vision-based approach that can quantify the accuracy of estimations by considering the spatial structure embedded in the estimated fields. Our new approach consists of adapting a new metric, Color Coherent Vectors (CCV), to evaluate the accuracy of estimated fields achieved by EnKF. CCV is a histogram-based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three-channel images and use CCV to compare and quantify the accuracy of estimations. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of spatial data assimilation techniques. Under various factors of data assimilation methods such as number, layout, and type of measurements, we compare the performance of CCV with other metrics such as RMSE. By simulating hydrogeological processes using estimated and true fields, we observe that CCV outperforms other existing evaluation metrics.

Open-Source Digital Elevation Model (DEMs) Evaluation with GPS and LiDAR Data

NASA Astrophysics Data System (ADS)

Khalid, N. F.; Din, A. H. M.; Omar, K. M.; Khanan, M. F. A.; Omar, A. H.; Hamid, A. I. A.; Pa'suya, M. F.

2016-09-01

Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

The spatial accuracy of geographic ecological momentary assessment (GEMA): Error and bias due to subject and environmental characteristics.

PubMed

Mennis, Jeremy; Mason, Michael; Ambrus, Andreea; Way, Thomas; Henry, Kevin

2017-09-01

Geographic ecological momentary assessment (GEMA) combines ecological momentary assessment (EMA) with global positioning systems (GPS) and geographic information systems (GIS). This study evaluates the spatial accuracy of GEMA location data and bias due to subject and environmental data characteristics. Using data for 72 subjects enrolled in a study of urban adolescent substance use, we compared the GPS-based location of EMA responses in which the subject indicated they were at home to the geocoded home address. We calculated the percentage of EMA locations within a sixteenth, eighth, quarter, and half miles from the home, and the percentage within the same tract and block group as the home. We investigated if the accuracy measures were associated with subject demographics, substance use, and emotional dysregulation, as well as environmental characteristics of the home neighborhood. Half of all subjects had more than 88% of their EMA locations within a half mile, 72% within a quarter mile, 55% within an eighth mile, 50% within a sixteenth of a mile, 83% in the correct tract, and 71% in the correct block group. There were no significant associations with subject or environmental characteristics. Results support the use of GEMA for analyzing subjects' exposures to urban environments. Researchers should be aware of the issue of spatial accuracy inherent in GEMA, and interpret results accordingly. Understanding spatial accuracy is particularly relevant for the development of 'ecological momentary interventions' (EMI), which may depend on accurate location information, though issues of privacy protection remain a concern. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast simulation of yttrium-90 bremsstrahlung photons with GATE.

PubMed

Rault, Erwann; Staelens, Steven; Van Holen, Roel; De Beenhouwer, Jan; Vandenberghe, Stefaan

2010-06-01

Multiple investigators have recently reported the use of yttrium-90 (90Y) bremsstrahlung single photon emission computed tomography (SPECT) imaging for the dosimetry of targeted radionuclide therapies. Because Monte Carlo (MC) simulations are useful for studying SPECT imaging, this study investigates the MC simulation of 90Y bremsstrahlung photons in SPECT. To overcome the computationally expensive simulation of electrons, the authors propose a fast way to simulate the emission of 90Y bremsstrahlung photons based on prerecorded bremsstrahlung photon probability density functions (PDFs). The accuracy of bremsstrahlung photon simulation is evaluated in two steps. First, the validity of the fast bremsstrahlung photon generator is checked. To that end, fast and analog simulations of photons emitted from a 90Y point source in a water phantom are compared. The same setup is then used to verify the accuracy of the bremsstrahlung photon simulations, comparing the results obtained with PDFs generated from both simulated and measured data to measurements. In both cases, the energy spectra and point spread functions of the photons detected in a scintillation camera are used. Results show that the fast simulation method is responsible for a 5% overestimation of the low-energy fluence (below 75 keV) of the bremsstrahlung photons detected using a scintillation camera. The spatial distribution of the detected photons is, however, accurately reproduced with the fast method and a computational acceleration of approximately 17-fold is achieved. When measured PDFs are used in the simulations, the simulated energy spectrum of photons emitted from a point source of 90Y in a water phantom and detected in a scintillation camera closely approximates the measured spectrum. The PSF of the photons imaged in the 50-300 keV energy window is also accurately estimated with a 12.4% underestimation of the full width at half maximum and 4.5% underestimation of the full width at tenth maximum. Despite its limited accuracy, the fast bremsstrahlung photon generator is well suited for the simulation of bremsstrahlung photons emitted in large homogeneous organs, such as the liver, and detected in a scintillation camera. The computational acceleration makes it very useful for future investigations of 90Y bremsstrahlung SPECT imaging.

Accuracy verification of the photostereometric system KKN/1B developed for intraoperative measurement of knee movement immediately after total knee arthroplasty.

PubMed

Nishino, K; Hayashi, T; Suzuki, Y; Koga, Y; Omori, G

1999-01-01

The function and integrity of the knee joint following total knee arthroplasty (TKA) is determined at first by the design and implantation of the prosthesis, and later by the tension of soft tissues surrounding it. Accurate post-TKA motion data obtained intraoperatively could be used not only to optimize implantation techniques from a kinematic standpoint, but also to improve prosthetic design. We therefore developed a system specifically geared to photostereometric measurement of 6 d.o.f. knee motion. A total of eight LEDs are mounted on the prosthetic components in two sets of four by means of connecting measuring-bows. The positions of the LEDs are detected in three-dimensions by two sets of three linear CCD cameras, located bilaterally relative to the knee. The position and orientation of the femoral component relative to the tibial one are estimated from the positions of all LEDs in the sense of least-squares. Based upon results of various accuracy validation experiments performed after precise camera calibration, static overall accuracy and spatial resolution were considered to lie within 0.52 and 0.11 mm, respectively, at any point on the femoral articular surface.

High accuracy transit photometry of the planet OGLE-TR-113b with a new deconvolution-based method

NASA Astrophysics Data System (ADS)

Gillon, M.; Pont, F.; Moutou, C.; Bouchy, F.; Courbin, F.; Sohy, S.; Magain, P.

2006-11-01

A high accuracy photometry algorithm is needed to take full advantage of the potential of the transit method for the characterization of exoplanets, especially in deep crowded fields. It has to reduce to the lowest possible level the negative influence of systematic effects on the photometric accuracy. It should also be able to cope with a high level of crowding and with large-scale variations of the spatial resolution from one image to another. A recent deconvolution-based photometry algorithm fulfills all these requirements, and it also increases the resolution of astronomical images, which is an important advantage for the detection of blends and the discrimination of false positives in transit photometry. We made some changes to this algorithm to optimize it for transit photometry and used it to reduce NTT/SUSI2 observations of two transits of OGLE-TR-113b. This reduction has led to two very high precision transit light curves with a low level of systematic residuals, used together with former photometric and spectroscopic measurements to derive new stellar and planetary parameters in excellent agreement with previous ones, but significantly more precise.

A long-term Northern Hemisphere snow cover extent product (JASMES) deriving from satellite-borne optical sensors using consistent objective criteria

NASA Astrophysics Data System (ADS)

Hori, M.; Sugiura, K.; Kobayashi, K.; Aoki, T.; Tanikawa, T.; Niwano, M.; Enomoto, H.

2017-12-01

A long-term Northern Hemisphere (NH) snow cover extent (SCE) product (JASMES SCE) was developed from the application of a consistent objective snow cover mapping algorithm to satellite-borne optical sensors (NOAA/AVHRR and NASA's optical sensor MODIS) from 1982 to the present. We estimated NH SCE from weekly composited snow cover maps and evaluated the accuracies of snow cover detection using in-situ snow data. As benchmark SCE product, we also evaluated the accuracy of SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed that JASMES SCE has more temporally stable accuracies. Seasonally averaged SCE derived from JASMES exhibited negative slopes in all seasons which is opposite to those of NOAA-CDR SCE in the fall and winter seasons. The spatial pattern of annual snow cover duration (SCD) trends exhibited noticeable asymmetric pattern between continents with the largest negative trends seen over western Eurasia. The NH SCE product will be connected to the data of the Japanese Earth Observing satellite named "Global Change Observation Mission for Climate (GCOM-C)" to be launched in late 2017.

Burn-injured tissue detection for debridement surgery through the combination of non-invasive optical imaging techniques.

PubMed

Heredia-Juesas, Juan; Thatcher, Jeffrey E; Lu, Yang; Squiers, John J; King, Darlene; Fan, Wensheng; DiMaio, J Michael; Martinez-Lorenzo, Jose A

2018-04-01

The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion.

PubMed

Steger, Doris; Berry, David; Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.

Systematic Spatial Bias in DNA Microarray Hybridization Is Caused by Probe Spot Position-Dependent Variability in Lateral Diffusion

PubMed Central

Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

Background The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. Methodology/Principal Findings This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Conclusions Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization. PMID:21858215

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

PubMed

Kim, Sehwi; Jung, Inkyung

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data

PubMed Central

Kim, Sehwi

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns. PMID:28753674

Automated thematic mapping and change detection of ERTS-A images. [farmlands, cities, and mountain identification in Utah, Washington, Arizona, and California

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1974-01-01

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Ground Testing Strategies for Verifying the Slew Rate Tolerance of Star Trackers

PubMed Central

Dzamba, Tom; Enright, John

2014-01-01

The performance of a star tracker is largely based on the availability of its attitude solution. Several methods exist to assess star tracker availability under both static and dynamic imaging conditions. However, these methods typically make various idealizations that can limit the accuracy of these results. This study aims to increase the fidelity of star tracker availability modeling by accounting for the effects of detection logic and pixel saturation on star detection. We achieve this by developing an analytical model for the focal plane intensity distribution of a star in the presence of sensor slew. Using the developed model, we examine the effects of slew rate on star detection using simulations and lab tests. The developed approach allows us to determine the maximum slew rate for which a star of a given stellar magnitude can still be detected. This information can then be used to describe the availability of a star tracker attitude solution as a function of slew rate, both spatially, across the entire celestial sphere, or locally, along a specified orientation track. PMID:24577522

Gender differences in multitasking reflect spatial ability.

PubMed

Mäntylä, Timo

2013-04-01

Demands involving the scheduling and interleaving of multiple activities have become increasingly prevalent, especially for women in both their paid and unpaid work hours. Despite the ubiquity of everyday requirements to multitask, individual and gender-related differences in multitasking have gained minimal attention in past research. In two experiments, participants completed a multitasking session with four gender-fair monitoring tasks and separate tasks measuring executive functioning (working memory updating) and spatial ability (mental rotation). In both experiments, males outperformed females in monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of monitoring accuracy, but only spatial ability mediated gender differences in multitasking. Menstrual changes accentuated these effects, such that gender differences in multitasking (and spatial ability) were eliminated between males and females who were in the menstrual phase of the menstrual cycle but not between males and females who were in the luteal phase. These findings suggest that multitasking involves spatiotemporal task coordination and that gender differences in multiple-task performance reflect differences in spatial ability.

Daily monitoring of 30 m crop condition over complex agricultural landscapes

NASA Astrophysics Data System (ADS)

Sun, L.; Gao, F.; Xie, D.; Anderson, M. C.; Yang, Y.

2017-12-01

Crop progress provides information necessary for efficient irrigation, scheduling fertilization and harvesting operations at optimal times for achieving higher yields. In the United States, crop progress reports are released online weekly by US Department of Agriculture (USDA) - National Agricultural Statistics Service (NASS). However, the ground data collection is time consuming and subjective, and these reports are provided at either district (multiple counties) or state level. Remote sensing technologies have been widely used to map crop conditions, to extract crop phenology, and to predict crop yield. However, for current satellite-based sensors, it is difficult to acquire both high spatial resolution and frequent coverage. For example, Landsat satellites are capable to capture 30 m resolution images, while the long revisit cycles, cloud contamination further limited their use in detecting rapid surface changes. On the other hand, MODIS can provide daily observations, but with coarse spatial resolutions range from 250 to 1000 m. In recent years, multi-satellite data fusion technology such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used to combine the spatial resolution of Landsat with the temporal frequency of MODIS. It has been found that this synthetic dataset could provide more valuable information compared to the images acquired from only one single sensor. However, accuracy of STARFM depends on heterogeneity of landscape and available clear image pairs of MODIS and Landsat. In this study, a new fusion method was developed using the crop vegetation index (VI) timeseries extracted from "pure" MODIS pixels and Landsat overpass images to generate daily 30 m VI for crops. The fusion accuracy was validated by comparing to the original Landsat images. Results show that the relative error in non-rapid growing period is around 3-5% and in rapid growing period is around 6-8% . The accuracy is much better than that of STARFM which is 4-9% in non-rapid growing period and 10-16% in rapid growing period based on 13 image pairs. The predicted VI from this approach looks consistent and smooth in the SLC-off gap stripes of Landsat 7 ETM+ image. The new fusion results will be used to map crop phenology and to predict crop yield at field scale in the complex agricultural landscapes.

Deconstructing Building Blocks: Preschoolers' Spatial Assembly Performance Relates to Early Mathematical Skills

ERIC Educational Resources Information Center

Verdine, Brian N.; Golinkoff, Roberta M.; Hirsh-Pasek, Kathryn; Newcombe, Nora S.; Filipowicz, Andrew T.; Chang, Alicia

2014-01-01

This study focuses on three main goals: First, 3-year-olds' spatial assembly skills are probed using interlocking block constructions (N = 102). A detailed scoring scheme provides insight into early spatial processing and offers information beyond a basic accuracy score. Second, the relation of spatial assembly to early mathematical skills…

Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

2017-11-01

Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework.

PubMed

Rahmati, Omid; Tahmasebipour, Naser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

2017-02-01

Despite the importance of soil erosion in sustainable development goals in arid and semi-arid areas, the study of the geo-environmental conditions and factors influencing gully erosion occurrence is rarely undertaken. As effort to this challenge, the main objective of this study is to apply an integrated approach of Geographic Object-Based Image Analysis (GEOBIA) together with high-spatial resolution imagery (SPOT-5) for detecting gully erosion features at the Kashkan-Poldokhtar watershed, Iran. We also aimed to apply a Conditional Probability (CP) model for establishing the spatial relationship between gullies and the Geo-Environmental Factors (GEFs). The gully erosion inventory map prepared using GEOBIA and field surveying was randomly partitioned into two subsets: (1) part 1 that contains 70% was used in the training phase of the CP model; (2) part 2 is a validation dataset (30%) for validation of the model and to confirm its accuracy. Prediction performances of the GEOBIA and CP model were checked by overall accuracy and Receiver Operating Characteristics (ROC) curve methods, respectively. In addition, the influence of all GEFs on gully erosion was evaluated by performing a sensitivity analysis model. The validation findings illustrated that overall accuracy for GEOBIA approach and the area under the ROC curve for the CP model were 92.4% and 89.9%, respectively. Also, based on sensitivity analysis, soil texture, drainage density, and lithology represent significantly effects on the gully erosion occurrence. This study has shown that the integrated framework can be successfully used for modeling gully erosion occurrence in a data-poor environment. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimized statistical parametric mapping for partial-volume-corrected amyloid positron emission tomography in patients with Alzheimer's disease and Lewy body dementia

NASA Astrophysics Data System (ADS)

Oh, Jungsu S.; Kim, Jae Seung; Chae, Sun Young; Oh, Minyoung; Oh, Seung Jun; Cha, Seung Nam; Chang, Ho-Jong; Lee, Chong Sik; Lee, Jae Hong

2017-03-01

We present an optimized voxelwise statistical parametric mapping (SPM) of partial-volume (PV)-corrected positron emission tomography (PET) of 11C Pittsburgh Compound B (PiB), incorporating the anatomical precision of magnetic resonance image (MRI) and amyloid β (A β) burden-specificity of PiB PET. First, we applied region-based partial-volume correction (PVC), termed the geometric transfer matrix (GTM) method, to PiB PET, creating MRI-based lobar parcels filled with mean PiB uptakes. Then, we conducted a voxelwise PVC by multiplying the original PET by the ratio of a GTM-based PV-corrected PET to a 6-mm-smoothed PV-corrected PET. Finally, we conducted spatial normalizations of the PV-corrected PETs onto the study-specific template. As such, we increased the accuracy of the SPM normalization and the tissue specificity of SPM results. Moreover, lobar smoothing (instead of whole-brain smoothing) was applied to increase the signal-to-noise ratio in the image without degrading the tissue specificity. Thereby, we could optimize a voxelwise group comparison between subjects with high and normal A β burdens (from 10 patients with Alzheimer's disease, 30 patients with Lewy body dementia, and 9 normal controls). Our SPM framework outperformed than the conventional one in terms of the accuracy of the spatial normalization (85% of maximum likelihood tissue classification volume) and the tissue specificity (larger gray matter, and smaller cerebrospinal fluid volume fraction from the SPM results). Our SPM framework optimized the SPM of a PV-corrected A β PET in terms of anatomical precision, normalization accuracy, and tissue specificity, resulting in better detection and localization of A β burdens in patients with Alzheimer's disease and Lewy body dementia.

Accuracy in breast shape alignment with 3D surface fitting algorithms.

PubMed

Riboldi, Marco; Gierga, David P; Chen, George T Y; Baroni, Guido

2009-04-01

Surface imaging is in use in radiotherapy clinical practice for patient setup optimization and monitoring. Breast alignment is accomplished by searching for a tentative spatial correspondence between the reference and daily surface shape models. In this study, the authors quantify whole breast shape alignment by relying on texture features digitized on 3D surface models. Texture feature localization was validated through repeated measurements in a silicone breast phantom, mounted on a high precision mechanical stage. Clinical investigations on breast shape alignment included 133 fractions in 18 patients treated with accelerated partial breast irradiation. The breast shape was detected with a 3D video based surface imaging system so that breathing was compensated. An in-house algorithm for breast alignment, based on surface fitting constrained by nipple matching (constrained surface fitting), was applied. Results were compared with a commercial software where no constraints are utilized (unconstrained surface fitting). Texture feature localization was validated within 2 mm in each anatomical direction. Clinical data show that unconstrained surface fitting achieves adequate accuracy in most cases, though nipple mismatch is considerably higher than residual surface distances (3.9 mm vs 0.6 mm on average). Outliers beyond 1 cm can be experienced as the result of a degenerate surface fit, where unconstrained surface fitting is not sufficient to establish spatial correspondence. In the constrained surface fitting algorithm, average surface mismatch within 1 mm was obtained when nipple position was forced to match in the [1.5; 5] mm range. In conclusion, optimal results can be obtained by trading off the desired overall surface congruence vs matching of selected landmarks (constraint). Constrained surface fitting is put forward to represent an improvement in setup accuracy for those applications where whole breast positional reproducibility is an issue.

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

USGS Publications Warehouse

Landguth, Erin L.; Gedy, Bradley C.; Oyler-McCance, Sara J.; Garey, Andrew L.; Emel, Sarah L.; Mumma, Matthew; Wagner, Helene H.; Fortin, Marie-Josée; Cushman, Samuel A.

2012-01-01

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals.

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

USGS Publications Warehouse

Landguth, E.L.; Fedy, B.C.; Oyler-McCance, S.J.; Garey, A.L.; Emel, S.L.; Mumma, M.; Wagner, H.H.; Fortin, M.-J.; Cushman, S.A.

2012-01-01

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals. ?? 2011 Blackwell Publishing Ltd.

Providing a Spatial Context for Crop Insurance in Ethiopia: Multiscale Comparisons of Vegetation Metrics in Tigray

NASA Astrophysics Data System (ADS)

Mann, B. F.; Small, C.

2014-12-01

Weather-based index insurance projects are rapidly expanding across the developing world. Many of these projects use satellite-based observations to detect extreme weather events, which inform and trigger payouts to smallholder farmers. While most index insurance programs use precipitation measurements to determine payouts, the use of remotely sensed observations of vegetation is currently being explored. In order to use vegetation indices as a basis for payouts, it is necessary to establish a consistent relationship between the vegetation index and the health and abundance of agriculture on the ground. The accuracy with which remotely sensed vegetation indices can detect changes in agriculture depends on both the spatial scale of the agriculture and the spatial resolution of the sensor. This study analyzes the relationship between meter and decameter scale vegetation fraction estimates derived from linear spectral mixture models with a more commonly used vegetation index (NDVI, EVI) at hectometer spatial scales. In addition, the analysis incorporates land cover/land use field observations collected in Tigray Ethiopia in July 2013. . It also tests the flexibility and utility of a standardized spectral mixture model in which land cover is represented as continuous fields of rock and soil substrate (S), vegetation (V) and dark surfaces (D; water, shadow). This analysis found strong linear relationships with vegetation metrics at 1.6-meter, 30-meter and 250-meter resolutions across spectrally diverse subsets of Tigray, Ethiopia and significantly correlated relationships using the Spearman's rho statistic. The observed linear scaling has positive implications for future use of moderate resolution vegetation indices in similar landscapes; especially index insurance projects that are scaling up across the developing world using remotely-sensed environmental information.

Locally distributed ground deformation in an area of potential phreatic eruption, Midagahara volcano, Japan, detected by single-look-based InSAR time series analysis

NASA Astrophysics Data System (ADS)

Kobayashi, Tomokazu

2018-05-01

Although it is difficult to monitor the spatial extent and temporal evolution of local and small-magnitude ground inflation, this information is vital to assess the potential for phreatic eruption. Herein, we demonstrate the detection of locally distributed ground deformation preceding the enhancement of geothermal activity in the Midagahara volcano, Japan, through the application of single-look-based interferometric synthetic aperture radar analysis. In the Jigoku-dani geothermal area, the ground deformation proceeded at a low speed of 4 cm/year at most with a spatial extent of 500 m in the east-west direction and 250 m in the north-south direction. The deformation can be recognized to progress from 2007, at the latest, to 2010, after which the geothermal activity increased, with the collapse of sulfur towers and the appearance of active fumaroles and boiling water on the ground surface. The most deformed area corresponds to the geothermal area with the highest activity observed on the ground surface. Assuming a sill opening model, the deformation source is estimated to be located at a depth of 50 m from the surface with a speed of 7 cm/year at most, which is consistent with the depth of the highly conductive medium inferred from magnetotelluric analyses. This may suggest that volcanic fluid and/or heat was injected into the fluid-rich medium from depth and caused the ground inflation. Our results demonstrate that high-spatial-resolution deformation data can be an effective tool to monitor subsurface pressure conditions with pinpoint spatial accuracy during the build-up to phreatic eruptions.

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

Temporal and spatial binning of TCSPC data to improve signal-to-noise ratio and imaging speed

NASA Astrophysics Data System (ADS)

Walsh, Alex J.; Beier, Hope T.

2016-03-01

Time-correlated single photon counting (TCSPC) is the most robust method for fluorescence lifetime imaging using laser scanning microscopes. However, TCSPC is inherently slow making it ineffective to capture rapid events due to the single photon product per laser pulse causing extensive acquisition time limitations and the requirement of low fluorescence emission efficiency to avoid bias of measurement towards short lifetimes. Furthermore, thousands of photons per pixel are required for traditional instrument response deconvolution and fluorescence lifetime exponential decay estimation. Instrument response deconvolution and fluorescence exponential decay estimation can be performed in several ways including iterative least squares minimization and Laguerre deconvolution. This paper compares the limitations and accuracy of these fluorescence decay analysis techniques to accurately estimate double exponential decays across many data characteristics including various lifetime values, lifetime component weights, signal-to-noise ratios, and number of photons detected. Furthermore, techniques to improve data fitting, including binning data temporally and spatially, are evaluated as methods to improve decay fits and reduce image acquisition time. Simulation results demonstrate that binning temporally to 36 or 42 time bins, improves accuracy of fits for low photon count data. Such a technique reduces the required number of photons for accurate component estimation if lifetime values are known, such as for commercial fluorescent dyes and FRET experiments, and improve imaging speed 10-fold.

Spatial/Spectral Identification of Endmembers from AVIRIS Data using Mathematical Morphology

NASA Technical Reports Server (NTRS)

Plaza, Antonio; Martinez, Pablo; Gualtieri, J. Anthony; Perez, Rosa M.

2001-01-01

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Social Eavesdropping: Can You Hear the Emotionality in a "Hello" That Is Not Meant for You?

PubMed

Karthikeyan, Sethu; Ramachandra, Vijayachandra

2017-01-01

The study examined third-party listeners' ability to detect the Hellos spoken to prevalidated happy, neutral, and sad facial expressions. The average detection accuracies from the happy and sad (HS), happy and neutral (HN), and sad and neutral (SN) listening tests followed the average vocal pitch differences between the two sets of Hellos in each of the tests; HS and HN detection accuracies were above chance reflecting the significant pitch differences between the respective Hellos. The SN detection accuracy was at chance reflecting the lack of pitch difference between sad and neutral Hellos. As expected, the SN detection accuracy positively correlated with theory of mind; participating in these tests has been likened to the act of eavesdropping, which has been discussed from an evolutionary perspective. An unexpected negative correlation between the HS detection accuracy and the empathy quotient has been discussed with respect to autism research on empathy and pitch discrimination.

The effects of age and workload on 3D spatial attention in dual-task driving.

PubMed

Pierce, Russell S; Andersen, George J

2014-06-01

In the present study we assessed whether the limits in visual-spatial attention associated with aging affect the spatial extent of attention in depth during driving performance. Drivers in the present study performed a car-following and light-detection task. To assess the extent of visual-spatial attention, we compared reaction times and accuracy to light change targets that varied in horizontal position and depth location. In addition, because workload has been identified as a factor that can change the horizontal and vertical extent of attention, we tested whether variability of the lead car speed influenced the extent of spatial attention for younger or older drivers. For younger drivers, reaction time (RT) to light-change targets varied as a function of distance and horizontal position. For older drivers RT varied only as a function of distance. There was a distance by horizontal position interaction for younger drivers but not for older drivers. Specifically, there was no effect of horizontal position at any given level of depth for older drivers. However, for younger drivers there was an effect of horizontal position for targets further in depth but not for targets nearer in depth. With regards to workload, we found no statistically reliable evidence that variability of the lead car speed had an effect on the spatial extent of attention for younger or older drivers. In a control experiment, we examined the effects of depth on light detection when the projected size and position of the targets was constant. Consistent with our previous results, we found that drivers' reaction time to light-change targets varied as a function of distance even when 2D position and size were controlled. Given that depth is an important dimension in driving performance, an important issue for assessing driving safety is to consider the limits of attention in the depth dimension. Therefore, we suggest that future research should consider the importance of depth as a dimension of spatial attention in relation to the assessment of driving performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

Evaluating the Impact of Spatial Resolution of Landsat Predictors on the Accuracy of Biomass Models for Large-area Estimation Across the Eastern USA

NASA Astrophysics Data System (ADS)

Deo, R. K.; Domke, G. M.; Russell, M.; Woodall, C. W.

2017-12-01

Landsat data have been widely used to support strategic forest inventory and management decisions despite the limited success of passive optical remote sensing for accurate estimation of aboveground biomass (AGB). The archive of publicly available Landsat data, available at 30-m spatial resolutions since 1984, has been a valuable resource for cost-effective large-area estimation of AGB to inform national requirements such as for the US national greenhouse gas inventory (NGHGI). In addition, other optical satellite data such as MODIS imagery of wider spatial coverage and higher temporal resolution are enriching the domain of spatial predictors for regional scale mapping of AGB. Because NGHGIs require national scale AGB information and there are tradeoffs in the prediction accuracy versus operational efficiency of Landsat, this study evaluated the impact of various resolutions of Landsat predictors on the accuracy of regional AGB models across three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We used recent national forest inventory (NFI) data with numerous Landsat-derived predictors at ten different spatial resolutions ranging from 30 to 1000 m to understand the optimal spatial resolution of the optical data for enhanced spatial inventory of AGB for NGHGI reporting. Ten generic spatial models at different spatial resolutions were developed for all sites and large-area estimates were evaluated (i) at the county-level against the independent designed-based estimates via the US NFI Evalidator tool and (ii) within a large number of strips ( 1 km wide) predicted via LiDAR metrics at a high spatial resolution. The county-level estimates by the Evalidator and Landsat models were statistically equivalent and produced coefficients of determination (R2) above 0.85 that varied with sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of decreasing resolutions. The Landsat-based total AGB estimates within the strips against the total AGB obtained using LiDAR metrics did not differ significantly and were within ±15 Mg/ha for each of the sites. We conclude that the optical satellite data at resolutions up to 1000 m provide acceptable accuracy for the US' NGHGI.

Spatial and Temporal Patterns of Unburned Areas within Fire Perimeters in the Northwestern United States from 1984 to 2014

NASA Astrophysics Data System (ADS)

Meddens, A. J.; Kolden, C.; Lutz, J. A.; Abatzoglou, J. T.; Hudak, A. T.

2016-12-01

Recently, there has been concern about increasing extent and severity of wildfires across the globe given rapid climate change. Areas that do not burn within fire perimeters can act as fire refugia, providing (1) protection from the detrimental effects of the fire, (2) seed sources, and (3) post-fire habitat on the landscape. However, recent studies have mainly focused on the higher end of the burn severity spectrum whereas the lower end of the burn severity spectrum has been largely ignored. We developed a spatially explicit database for 2,200 fires across the inland northwestern USA, delineating unburned areas within fire perimeters from 1984 to 2014. We used 1,600 Landsat scenes with one or two scenes before and one or two scenes after the fires to capture the unburned proportion of the fire. Subsequently, we characterized the spatial and temporal patterns of unburned areas and related the unburned proportion to interannual climate variability. The overall classification accuracy detecting unburned locations was 89.2% using a 10-fold cross-validation classification tree approach in combination with 719 randomly located field plots. The unburned proportion ranged from 2% to 58% with an average of 19% for a select number of fires. We find that using both an immediate post-fire image and a one-year post fire image improves classification accuracy of unburned islands over using just a single post-fire image. The spatial characteristics of the unburned islands differ between forested and non-forested regions with a larger amount of unburned area within non-forest. In addition, we show trends of unburned proportion related primarily to concurrent climatic drought conditions across the entire region. This database is important for subsequent analyses of fire refugia prioritization, vegetation recovery studies, ecosystem resilience, and forest management to facilitate unburned islands through fuels breaks, prescribed burning, and fire suppression strategies.