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Sample records for multispectral image classification

  1. Retinex Preprocessing for Improved Multi-Spectral Image Classification

    NASA Technical Reports Server (NTRS)

    Thompson, B.; Rahman, Z.; Park, S.

    2000-01-01

    The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original

  2. Classification of high dimensional multispectral image data

    NASA Technical Reports Server (NTRS)

    Hoffbeck, Joseph P.; Landgrebe, David A.

    1993-01-01

    A method for classifying high dimensional remote sensing data is described. The technique uses a radiometric adjustment to allow a human operator to identify and label training pixels by visually comparing the remotely sensed spectra to laboratory reflectance spectra. Training pixels for material without obvious spectral features are identified by traditional means. Features which are effective for discriminating between the classes are then derived from the original radiance data and used to classify the scene. This technique is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data taken over Cuprite, Nevada in 1992, and the results are compared to an existing geologic map. This technique performed well even with noisy data and the fact that some of the materials in the scene lack absorption features. No adjustment for the atmosphere or other scene variables was made to the data classified. While the experimental results compare favorably with an existing geologic map, the primary purpose of this research was to demonstrate the classification method, as compared to the geology of the Cuprite scene.

  3. Implementation of Multispectral Image Classification on a Remote Adaptive Computer

    NASA Technical Reports Server (NTRS)

    Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

    1999-01-01

    As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

  4. Phase classification by mean shift clustering of multispectral materials images.

    PubMed

    Martins, Diego Schmaedech; Josa, Victor M Galván; Castellano, Gustavo; da Costa, José A T Borges

    2013-10-01

    A mean-shift clustering (MSC) algorithm is introduced as a valuable alternative to perform materials phase classification from multispectral images. As opposed to other multivariate statistical techniques, such as factor analysis or principal component analysis (PCA), clustering techniques directly assign a class label to each pixel, so that their outputs are phase segmented images, i.e., there is no need for an additional segmentation algorithm. On the other hand, as compared to other clustering procedures and classification methods, such as segmentation by thresholding of multiple spectral components, MSC has the advantages of not requiring previous knowledge of the number of data clusters and not assuming any shape for these clusters, i.e., neither the number nor the composition of the phases must be previously known. This makes MSC a particularly useful tool for exploratory research, assisting phase identification of unknown samples. Visualization and interpretation of the results are also simplified, since the information content of the output image does not depend on the particular choice of the content of the color channels.We applied MSC to the analysis of two sets of X-ray maps acquired in scanning electron microscopes equipped with energy-dispersive detection systems. Our results indicate that MSC is capable of detecting additional phases, not clearly identified through PCA or multiple thresholding, with a very low empirical reject rate.

  5. Classification of emerald based on multispectral image and PCA

    NASA Astrophysics Data System (ADS)

    Yang, Weiping; Zhao, Dazun; Huang, Qingmei; Ren, Pengyuan; Feng, Jie; Zhang, Xiaoyan

    2005-02-01

    Traditionally, the grade discrimination and classifying of bowlders (emeralds) are implemented by using methods based on people's experiences. In our previous works, a method based on NCS(Natural Color System) color system and sRGB color space conversion is employed for a coarse grade classification of emeralds. However, it is well known that the color match of two colors is not a true "match" unless their spectra are the same. Because metameric colors can not be differentiated by a three channel(RGB) camera, a multispectral camera(MSC) is used as image capturing device in this paper. It consists of a trichromatic digital camera and a set of wide-band filters. The spectra are obtained by measuring a series of natural bowlders(emeralds) samples. Principal component analysis(PCA) method is employed to get some spectral eigenvectors. During the fine classification, the color difference and RMS of spectrum difference between estimated and original spectra are used as criterion. It has been shown that 6 eigenvectors are enough to reconstruct reflection spectra of the testing samples.

  6. Classification of multispectral image data by extraction and classification of homogeneous objects

    NASA Technical Reports Server (NTRS)

    Kettig, R. L.; Landgrebe, D. A.

    1975-01-01

    A method of classification of digitized multispectral image data is described. It is designed to exploit a particular type of dependence between adjacent states of nature that is characteristic of the data. The advantages of this, as opposed to the conventional per point approach, are greater accuracy and efficiency, and the results are in a more desirable form for most purposes. Experimental results from both aircraft and satellite data are included.

  7. Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

    PubMed Central

    Boucheron, Laura E; Bi, Zhiqiang; Harvey, Neal R; Manjunath, BS; Rimm, David L

    2007-01-01

    Background We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. Results For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. Conclusion Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery. PMID:17634098

  8. Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects

    NASA Technical Reports Server (NTRS)

    Kettig, R. L.

    1975-01-01

    A method of classification of digitized multispectral images is developed and experimentally evaluated on actual earth resources data collected by aircraft and satellite. The method is designed to exploit the characteristic dependence between adjacent states of nature that is neglected by the more conventional simple-symmetric decision rule. Thus contextual information is incorporated into the classification scheme. The principle reason for doing this is to improve the accuracy of the classification. For general types of dependence this would generally require more computation per resolution element than the simple-symmetric classifier. But when the dependence occurs in the form of redundance, the elements can be classified collectively, in groups, therby reducing the number of classifications required.

  9. Multispectral image analysis for object recognition and classification

    NASA Astrophysics Data System (ADS)

    Viau, C. R.; Payeur, P.; Cretu, A.-M.

    2016-05-01

    Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

  10. A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

    NASA Technical Reports Server (NTRS)

    Emerson, Charles W.; Sig-NganLam, Nina; Quattrochi, Dale A.

    2004-01-01

    The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

  11. Fusion of Hyperspectral and Vhr Multispectral Image Classifications in Urban Areas

    NASA Astrophysics Data System (ADS)

    Hervieu, Alexandre; Le Bris, Arnaud; Mallet, Clément

    2016-06-01

    An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with ?-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification.

  12. Improved image classification with neural networks by fusing multispectral signatures with topological data

    NASA Technical Reports Server (NTRS)

    Harston, Craig; Schumacher, Chris

    1992-01-01

    Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent.

  13. Classification of multispectral satellite image data using improved NRBF neural networks

    NASA Astrophysics Data System (ADS)

    Tao, Xiaoli; Michel, Howard E.

    2003-10-01

    This paper describes a novel classification technique-NRBF (Normalized Radial Basis Function) neural network classifier based on spectral clustering methods. The spectral method is used in the unsupervised learning part of the NRBF neural networks. Compared with other general clustering methods used in NRBF neural networks, such as KMeans, the spectral method can avoid the local minima problem and therefore multiple restarts are not necessary to obtain a good solution. This classifier was tested with satellite multi-spectral image data of New England acquired by Landsat 7 ETM+ sensors. Classification results show that this new neural network model is more accurate and robust than the conventional RBF model. Furthermore, we analyze how the number of the hidden units affects training and testing accuracy. These results suggest that this new model may be an effective method for classification of multispectral satellite image data.

  14. Contextual classification of multispectral image data: Approximate algorithm

    NASA Technical Reports Server (NTRS)

    Tilton, J. C. (Principal Investigator)

    1980-01-01

    An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced.

  15. Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance.

    PubMed

    Bautista, Pinky A; Abe, Tokiya; Yamaguchi, Masahiro; Yagi, Yukako; Ohyama, Nagaaki

    2005-12-01

    In this study, the digital transformation (digital staining) of the 16-band multispectral image of a hematoxylin and eosin (HE) stained pathological specimen to its Masson's trichrome (MT) stained counterpart is addressed. The digital staining procedure involves the classification of the various H&E-stained tissue components and then the transformation of their transmittance spectra to their equivalent MT-stained transmittance configurations. Combination of transmittance classifiers were designed to classify the various tissue components found in the multispectral images of an HE-stained specimen, e.g. nucleus, cytoplasm, red blood cell (RBC), fibrosis, etc.; while pseudo-inverse method was used to obtain the transformation matrices that would translate the transmittance spectra of the classified HE-stained multispectral pixels to their MT-stained configurations. To generate the digitally stained image, weighting factors, which were based on the classifiers beliefs, were introduced to the generated transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) to implement a digital staining framework in the pathological context.

  16. Multispectral image classification of MRI data using an empirically-derived clustering algorithm

    SciTech Connect

    Horn, K.M.; Osbourn, G.C.; Bouchard, A.M.; Sanders, J.A. |

    1998-08-01

    Multispectral image analysis of magnetic resonance imaging (MRI) data has been performed using an empirically-derived clustering algorithm. This algorithm groups image pixels into distinct classes which exhibit similar response in the T{sub 2} 1st and 2nd-echo, and T{sub 1} (with ad without gadolinium) MRI images. The grouping is performed in an n-dimensional mathematical space; the n-dimensional volumes bounding each class define each specific tissue type. The classification results are rendered again in real-space by colored-coding each grouped class of pixels (associated with differing tissue types). This classification method is especially well suited for class volumes with complex boundary shapes, and is also expected to robustly detect abnormal tissue classes. The classification process is demonstrated using a three dimensional data set of MRI scans of a human brain tumor.

  17. [Classification of wetlands in multispectral remote sensing image based on HPSO and FCM].

    PubMed

    Jiang, Wei-Guo; Chen, Qiang; Guo, Ji; Tang, Hong; Li, Xue

    2010-12-01

    The present paper analyzed the characteristics of particle swarm optimization(PSO), hybrid particle swarm optimization (HPSO) and fuzzy C-means (FCM), imported FCM into HPSO, and improved the HPSO-FCM arithmetic. An HPSO-FCM program was developed using Fortran language in MATLAB. Besides, a synthesis image combined with the former three principal components was obtained through band stacking and principal component analysis, taking the multispectral visible image of HJ-1 Satellite shot in June 2009 and the ASAR radar image of ENVISAT as basic data. And the paper has done a wetlands classification experiment in the synthesis image of the East Dongting Lake of Hunan province, using HPSO-FCM arithmetic and ISODATA separately. The results indicated: (1) The arithmetic which imported crossover operator of genetic algorithms and FCM into HPSO had better search speed and convergent precision, and it could search and optimize the best cluster center more efficiently. (2) The HPSO-FCM arithmetic has better precision in wetlands classification in multispectral remote sensing image, and it is an effective method in remote sensing image classification.

  18. [Classification of wetlands in multispectral remote sensing image based on HPSO and FCM].

    PubMed

    Jiang, Wei-Guo; Chen, Qiang; Guo, Ji; Tang, Hong; Li, Xue

    2010-12-01

    The present paper analyzed the characteristics of particle swarm optimization(PSO), hybrid particle swarm optimization (HPSO) and fuzzy C-means (FCM), imported FCM into HPSO, and improved the HPSO-FCM arithmetic. An HPSO-FCM program was developed using Fortran language in MATLAB. Besides, a synthesis image combined with the former three principal components was obtained through band stacking and principal component analysis, taking the multispectral visible image of HJ-1 Satellite shot in June 2009 and the ASAR radar image of ENVISAT as basic data. And the paper has done a wetlands classification experiment in the synthesis image of the East Dongting Lake of Hunan province, using HPSO-FCM arithmetic and ISODATA separately. The results indicated: (1) The arithmetic which imported crossover operator of genetic algorithms and FCM into HPSO had better search speed and convergent precision, and it could search and optimize the best cluster center more efficiently. (2) The HPSO-FCM arithmetic has better precision in wetlands classification in multispectral remote sensing image, and it is an effective method in remote sensing image classification. PMID:21322233

  19. Automated classification of multi-spectral MR images using Linear Discriminant Analysis.

    PubMed

    Lin, Geng-Cheng; Wang, Wen-June; Wang, Chuin-Mu; Sun, Sheng-Yih

    2010-06-01

    Magnetic resonance imaging (MRI) is a valuable instrument in medical science owing to its capabilities in soft tissue characterization and 3D visualization. A potential application of MRI in clinical practice is brain parenchyma classification. This work proposes a novel approach called "Unsupervised Linear Discriminant Analysis (ULDA)" to classify and segment the three major tissues, i.e. gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), from a multi-spectral MR image of the human brain. The ULDA comprises two processes, namely Target Generation Process (TGP) and Linear Discriminant Analysis (LDA) classification. TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to train the optimal division boundary by LDA, such that three tissues GM, WM and CSF are separated. Finally, two sets of images, namely computer-generated phantom images and real MR images are used in the experiments to evaluate the effectiveness of ULDA. Experiment results reveal that UDLA segments a multi-spectral MR image much more effectively than either FMRIB's Automated Segmentation Tool (FAST) or Fuzzy C-means (FC). PMID:20044236

  20. GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES

    SciTech Connect

    S. PERKINS; ET AL

    2000-12-01

    We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximum likelihood classification and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or may be the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

  1. Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

    PubMed

    Medina, José M; Díaz, José A; Vukusic, Pete

    2015-04-20

    Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

  2. Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

    PubMed

    Medina, José M; Díaz, José A; Vukusic, Pete

    2015-04-20

    Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage. PMID:25969062

  3. Wavelet analysis and classification of urban environment using high-resolution multispectral image data

    NASA Astrophysics Data System (ADS)

    Myint, Soe Win

    2001-07-01

    Attempts to analyze urban features and classify land use and land cover directly from high-resolution satellite data with traditional computer classification techniques have proven to be inefficient. The fundamental problem usually found in identifying urban land cover types from high-resolution satellite imagery is that urban areas are composed of diverse materials (metal, glass, concrete, asphalt, plastic, trees, soil, etc.). These materials, each of which may have completely different spectral characteristics, are combined in complex ways by human beings. Hence, each urban land cover type may contain several different objects with different reflectance values. Noisy appearance with lots of edges, and the complex nature of these images, inhibit accurate interpretation of urban features. Traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture features play an important role in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. This study analyzed urban texture features in multi-spectral image data. Recent development in the mathematical theory of wavelet transform has received overwhelming attention by the image analysts. An evaluation of the ability of wavelet transform and other texture analysis algorithms in urban feature extraction and classification was performed in this study. Advanced Thermal Land Application Sensor (ATLAS) image data at 2.5 m spatial resolution acquired with 15 channel (0.45 mum--12.2 mum) were used for this research. The data were collected by a NASA Stennis LearJet 23 flying at 6600 feet over Baton Rouge, Louisiana, on May 7, 1999. The algorithms examined were the wavelet transforms, spatial co-occurrence matrix, fractal analysis, and spatial autocorrelation. The performance of the above approaches with the use of different window sizes, different

  4. Object-oriented classification using quasi-synchronous multispectral images (optical and radar) over agricultural surface

    NASA Astrophysics Data System (ADS)

    Marais Sicre, Claire; Baup, Frederic; Fieuzal, Remy

    2015-04-01

    over 214 plots during the MCM'10 experiment conducted by the CESBIO laboratory in 2010. Classifications performances have been evaluated considering two cases: using only one frequency in optical or microwave domain, or using a combination of several frequencies (mixed between optical and microwave). For the first case, best results were obtained using optical wavelength with mean overall accuracy (OA) of 84%, followed by Terrasar-X (HH) and Radarsat-2 (HV or HV) which respectively offer overall accuracies of 77% and 73%. Concerning the vegetation, wheat was well classified whatever the wavelength used (OA > 93%). Barley was more complicated to classified and could be mingled with wheat or grassland. Best results were obtained using of green, red, blue, X-band or L-band wavelength offering an OA superior to 45%. Radar images were clearly well adapted to identify rapeseed (OA > 83%), especially at C (VV, HH and HV) and X-band (HH). The accuracy of grassland classification never exceeded 79% and results were stable between frequencies (excepted at L-band: 51%). The three soil roughness states were quite well classified whatever the wavelength and performances decreased with the increase of soil roughness. The combine use of multi-frequencies increased performances of the classification. Overall accuracy reached respectively 83% and 96% for C-band full polarization and for Formosat-2 multispectral approaches.

  5. Multispectral fluorescence imaging of atherosclerosis

    SciTech Connect

    Davenport, C.M.C.

    1992-01-01

    Multispectral fluorescence imaging is a new diagnostic technique with the potential to provide improved detection and classification of atherosclerotic disease. This technique involves imaging the fluorescence response of a tissue region through a tunable band-pass filtering device. The result is a set of image in which each individual image is composed of the fluorescence emission within a specified band of wavelengths. Multispectral imaging combined with angioscopic technology allows direct access to important spectral information and spatial attributes providing the potential for more informed clinical decisions about which, if any, treatment modality is indicated. In this dissertation, the system requirements for an angioscopic system with multispectral imaging capability are identified. This analysis includes a description of the necessary optical components and their characteristics as well as the experimental determination of spectral radiance values for the fluorescence response of human aorta specimens and the estimation of anticipated signal-to-noise ratios for the spectral images. Other issues investigated include the number of spectral images required to provide good classification potential and the best normalization method to be utilized. Finally, the potential utility of the information contained within a multispectral data set is demonstrated. Two methods of utilizing the multispectral data are presented. The first method involves generating a ratio-image from the ratio of the intensities of two spectrally filtered images. The second method consists of using histologically verified training data to train a projector and then applying that projector to a set of spectral images. The result provides improved contrast image. White-light images (generated using an incandescent light source), total-fluorescence images (the fluorescence response without spectral filtering), ratio-images, and optimized contrast images are compared. T

  6. Multispectral imaging for biometrics

    NASA Astrophysics Data System (ADS)

    Rowe, Robert K.; Corcoran, Stephen P.; Nixon, Kristin A.; Ostrom, Robert E.

    2005-03-01

    Automated identification systems based on fingerprint images are subject to two significant types of error: an incorrect decision about the identity of a person due to a poor quality fingerprint image and incorrectly accepting a fingerprint image generated from an artificial sample or altered finger. This paper discusses the use of multispectral sensing as a means to collect additional information about a finger that significantly augments the information collected using a conventional fingerprint imager based on total internal reflectance. In the context of this paper, "multispectral sensing" is used broadly to denote a collection of images taken under different polarization conditions and illumination configurations, as well as using multiple wavelengths. Background information is provided on conventional fingerprint imaging. A multispectral imager for fingerprint imaging is then described and a means to combine the two imaging systems into a single unit is discussed. Results from an early-stage prototype of such a system are shown.

  7. Multispectral imaging and image processing

    NASA Astrophysics Data System (ADS)

    Klein, Julie

    2014-02-01

    The color accuracy of conventional RGB cameras is not sufficient for many color-critical applications. One of these applications, namely the measurement of color defects in yarns, is why Prof. Til Aach and the Institute of Image Processing and Computer Vision (RWTH Aachen University, Germany) started off with multispectral imaging. The first acquisition device was a camera using a monochrome sensor and seven bandpass color filters positioned sequentially in front of it. The camera allowed sampling the visible wavelength range more accurately and reconstructing the spectra for each acquired image position. An overview will be given over several optical and imaging aspects of the multispectral camera that have been investigated. For instance, optical aberrations caused by filters and camera lens deteriorate the quality of captured multispectral images. The different aberrations were analyzed thoroughly and compensated based on models for the optical elements and the imaging chain by utilizing image processing. With this compensation, geometrical distortions disappear and sharpness is enhanced, without reducing the color accuracy of multispectral images. Strong foundations in multispectral imaging were laid and a fruitful cooperation was initiated with Prof. Bernhard Hill. Current research topics like stereo multispectral imaging and goniometric multispectral measure- ments that are further explored with his expertise will also be presented in this work.

  8. D Land Cover Classification Based on Multispectral LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  9. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon

    SciTech Connect

    Adams, J.B.; Sabol, D.E.; Roberts, D.A.; Smith, M.O.; Gillespie, A.R.; Kapos, V.; Filho, R.A.

    1995-05-01

    Four time-sequential Landsat Thematic Mapper (TM) images of an area of Amazon forest, pasture, and second growth near Manaus, Brazil were classified according to dominant ground cover, using a new technique based on fractions of spectral endmembers. A simple four-endmember model consisting of reflectance spectra of green vegetation, nonphotosynthetic vegetation, soil, and shade was applied to all four images. Fractions of endmembers were used to define seven categories, each of which consisted of one or more classes of ground cover, where class names were based on field observations. Endmember fractions varied over time for many pixels, reflecting processes operating on the ground such as felling of forest, or regrowth of vegetation in previously cleared areas. Changes in classes over time were used to establish superclasses which grouped pixels having common histories. Sources of classification error were evaluated, including system noise, endmember variability, and low spectral contrast. Field work during each of the four years showed consistently high accuracy in per-image classification. Classification accuracy in any one year was improved by considering the multiyear context. Although the method was tested in the amazon basin, the results suggest that endmember classification may be generally useful for comparing multispectral images in space and time.

  10. Multispectral imaging probe

    DOEpatents

    Sandison, David R.; Platzbecker, Mark R.; Descour, Michael R.; Armour, David L.; Craig, Marcus J.; Richards-Kortum, Rebecca

    1999-01-01

    A multispectral imaging probe delivers a range of wavelengths of excitation light to a target and collects a range of expressed light wavelengths. The multispectral imaging probe is adapted for mobile use and use in confined spaces, and is sealed against the effects of hostile environments. The multispectral imaging probe comprises a housing that defines a sealed volume that is substantially sealed from the surrounding environment. A beam splitting device mounts within the sealed volume. Excitation light is directed to the beam splitting device, which directs the excitation light to a target. Expressed light from the target reaches the beam splitting device along a path coaxial with the path traveled by the excitation light from the beam splitting device to the target. The beam splitting device directs expressed light to a collection subsystem for delivery to a detector.

  11. Multispectral imaging probe

    DOEpatents

    Sandison, D.R.; Platzbecker, M.R.; Descour, M.R.; Armour, D.L.; Craig, M.J.; Richards-Kortum, R.

    1999-07-27

    A multispectral imaging probe delivers a range of wavelengths of excitation light to a target and collects a range of expressed light wavelengths. The multispectral imaging probe is adapted for mobile use and use in confined spaces, and is sealed against the effects of hostile environments. The multispectral imaging probe comprises a housing that defines a sealed volume that is substantially sealed from the surrounding environment. A beam splitting device mounts within the sealed volume. Excitation light is directed to the beam splitting device, which directs the excitation light to a target. Expressed light from the target reaches the beam splitting device along a path coaxial with the path traveled by the excitation light from the beam splitting device to the target. The beam splitting device directs expressed light to a collection subsystem for delivery to a detector. 8 figs.

  12. Studies on pansharpening and object-based classification of Worldview-2 multispectral image

    NASA Astrophysics Data System (ADS)

    Wyczałek, I.; Wyczałek, E.

    2013-12-01

    The new information contained in four additional spectral bands of high - resolution images from the satellite sensor WorldView - 2 should provide a visible improvement in the quality of analysis of large - scale phenomena occurring at the ground. Selected part of the image of Poznan was analyzed in order to verify these possibilities in relation to the urban environment. It includes riverside green area and a number of adjacent buildings. Attention has been focused on two components of object - oriented analysis - sharpening the image and its classification. In terms of pansharpening the aim was to obtain a clear picture of terrain objects in details, what should lead to the correct division of the image into homogenous segments and the subsequent fine classification. It was intended to ensure the possibility of separating small field objects within the set of classes. The task was carried out using various computer programs that enable the development and analysis of raster data (IDRISI Andes, ESRI ArcGIS 9.3, eCognition Developer 8) and some own computational modules. The main scientific objective of this study was to determine how much information from new spectral image layers after their pansharpening affects the quality of object - based classification of land cover in green and building areas of the city. As a basis for improving the quality of the classification was above mentioned ability of using additional data from new spectral bands of WorldView - 2 image. To assess the quality of the classification we used test that examines only the uncertain areas of t he picture, that is these which lie on differently classified types of land cover. The outcome of assessment confirmed the thesis of the positive albeit small impact of additional spectral channels on the result of object - based classification. But also pansharpening itself only slightly improves the quality of classified image

  13. Application of image classification techniques to multispectral lidar point cloud data

    NASA Astrophysics Data System (ADS)

    Miller, Chad I.; Thomas, Judson J.; Kim, Angela M.; Metcalf, Jeremy P.; Olsen, Richard C.

    2016-05-01

    Data from Optech Titan are analyzed here for purposes of terrain classification, adding the spectral data component to the lidar point cloud analysis. Nearest-neighbor sorting techniques are used to create the merged point cloud from the three channels. The merged point cloud is analyzed using spectral analysis techniques that allow for the exploitation of color, derived spectral products (pseudo-NDVI), as well as lidar features such as height values, and return number. Standard spectral image classification techniques are used to train a classifier, and analysis was done with a Maximum Likelihood supervised classification. Terrain classification results show an overall accuracy improvement of 10% and a kappa coefficient increase of 0.07 over a raster-based approach.

  14. IMPROVING THE ACCURACY OF HISTORIC SATELLITE IMAGE CLASSIFICATION BY COMBINING LOW-RESOLUTION MULTISPECTRAL DATA WITH HIGH-RESOLUTION PANCHROMATIC DATA

    SciTech Connect

    Getman, Daniel J

    2008-01-01

    Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic data (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.

  15. Multispectral rock-type separation and classification.

    SciTech Connect

    Moya, Mary M.; Fogler, Robert Joseph; Paskaleva, Biliana; Hayat, Majeed M.

    2004-06-01

    This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.

  16. Polarimetric Multispectral Imaging Technology

    NASA Technical Reports Server (NTRS)

    Cheng, L.-J.; Chao, T.-H.; Dowdy, M.; Mahoney, C.; Reyes, G.

    1993-01-01

    The Jet Propulsion Laboratory is developing a remote sensing technology on which a new generation of compact, lightweight, high-resolution, low-power, reliable, versatile, programmable scientific polarimetric multispectral imaging instruments can be built to meet the challenge of future planetary exploration missions. The instrument is based on the fast programmable acousto-optic tunable filter (AOTF) of tellurium dioxide (TeO2) that operates in the wavelength range of 0.4-5 microns. Basically, the AOTF multispectral imaging instrument measures incoming light intensity as a function of spatial coordinates, wavelength, and polarization. Its operation can be in either sequential, random access, or multiwavelength mode as required. This provides observation flexibility, allowing real-time alternation among desired observations, collecting needed data only, minimizing data transmission, and permitting implementation of new experiments. These will result in optimization of the mission performance with minimal resources. Recently we completed a polarimetric multispectral imaging prototype instrument and performed outdoor field experiments for evaluating application potentials of the technology. We also investigated potential improvements on AOTF performance to strengthen technology readiness for applications. This paper will give a status report on the technology and a prospect toward future planetary exploration.

  17. The trophic classification of lakes using ERTS multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Blackwell, R. J.; Boland, D. H.

    1975-01-01

    Lake classification methods based on the use of ERTS data are described. Preliminary classification results obtained by multispectral and digital image processing techniques indicate satisfactory correlation between ERTS data and EPA-supplied water analysis. Techniques for determining lake trophic levels using ERTS data are examined, and data obtained for 20 lakes are discussed.

  18. Cucumber disease diagnosis using multispectral images

    NASA Astrophysics Data System (ADS)

    Feng, Jie; Li, Hongning; Shi, Junsheng; Yang, Weiping; Liao, Ningfang

    2009-07-01

    In this paper, multispectral imaging technique for plant diseases diagnosis is presented. Firstly, multispectral imaging system is designed. This system utilizes 15 narrow-band filters, a panchromatic band, a monochrome CCD camera, and standard illumination observing environment. The spectral reflectance and color of 8 Macbeth color patches are reproduced between 400nm and 700nm in the process. In addition, spectral reflectance angle and color difference is obtained through measurements and analysis of color patches using spectrometer and multispectral imaging system. The result shows that 16 narrow-bands multispectral imaging system realizes good accuracy in spectral reflectance and color reproduction. Secondly, a horticultural plant, cucumber' familiar disease are the researching objects. 210 multispectral samples are obtained by multispectral and are classified by BP artificial neural network. The classification accuracies of Sphaerotheca fuliginea, Corynespora cassiicola, Pseudoperonospora cubensis are 100%. Trichothecium roseum and Cladosporium cucumerinum are 96.67% and 90.00%. It is confirmed that the multispectral imaging system realizes good accuracy in the cucumber diseases diagnosis.

  19. Multispectral thermal imaging

    SciTech Connect

    Weber, P.G.; Bender, S.C.; Borel, C.C.; Clodius, W.B.; Smith, B.W.; Garrett, A.; Pendergast, M.M.; Kay, R.R.

    1998-12-01

    Many remote sensing applications rely on imaging spectrometry. Here the authors use imaging spectrometry for thermal and multispectral signatures measured from a satellite platform enhanced with a combination of accurate calibrations and on-board data for correcting atmospheric distortions. The approach is supported by physics-based end-to-end modeling and analysis, which permits a cost-effective balance between various hardware and software aspects. The goal is to develop and demonstrate advanced technologies and analysis tools toward meeting the needs of the customer; at the same time, the attributes of this system can address other applications in such areas as environmental change, agriculture, and volcanology.

  20. A program system for efficient multispectral classification

    NASA Astrophysics Data System (ADS)

    Åkersten, S. I.

    Pixelwise multispectral classification is an important tool for analyzing remotely sensed imagery data. The computing time for performing this analysis becomes significantly large when large, multilayer images are analyzed. In the classical implementation of the supervised multispectral classification assuming gaussian-shaped multidimensional class-clusters, the computing time is furthermore approximately proportional to the square of the number of image layers. This leads to very appreciable CPU-times when large numbers of multispectral channels are used and/or temporal classification is performed. In order to decrease computer time, a classification program system has been implemented which has the following characteristics: (1) a simple one-dimensional box classifier, (2) a multidimensional box classifier, (3) a class-pivotal "canonical" classifier utilizing full maximum likelihood and making full use of within-class and between-class statistical characteristics, (4) a hybrid classifier (2 and 3 combined), and (5) a local neighbourhood filtering algorithm producing generalized classification results. The heart of the classifier is the class-pivotal canonical classifier. This algorithm is based upon an idea of Dye suggesting the use of linear transformations making possible a simultaneous evaluation of a measure of the pixel being likely not to belong to the candidate class as well as computing its full maximum likelihood ratio. In case it is more likely to be misclassified the full maximum likelihood evaluation can be truncated almost immediately, i.e. the candidate class can often be rejected using only one or two of the available transformed spectral features. The result of this is a classifier with CPU-time which is empirically shown to be linearly dependent upon the number of image layers. The use of the hybrid classifier lowers the CPU-time with another factor of 3-4. Furthermore, for certain problems like classifying water-non water a single spectral band

  1. MULTISPECTRAL THERMAL IMAGER - OVERVIEW

    SciTech Connect

    P. WEBER

    2001-03-01

    The Multispectral Thermal Imager satellite fills a new and important role in advancing the state of the art in remote sensing sciences. Initial results with the full calibration system operating indicate that the system was already close to achieving the very ambitious goals which we laid out in 1993, and we are confident of reaching all of these goals as we continue our research and improve our analyses. In addition to the DOE interests, the satellite is tasked about one-third of the time with requests from other users supporting research ranging from volcanology to atmospheric sciences.

  2. Multispectral Thermal Imager: overview

    NASA Astrophysics Data System (ADS)

    Bell, W. Randy; Weber, Paul G.

    2001-08-01

    The Multispectral Thermal Imager, MTI, is a research and development project sponsored by the United States Department of Energy. The primary mission is to demonstrate advanced multispectral and thermal imaging from a satellite, including new technologies, data processing and analysis techniques. The MTI builds on the efforts of a number of earlier efforts, including Landsat, NASA remote sensing missions, and others, but the MTI incorporates a unique combination of attributes. The MTI satellite was launched on 12 March 2000 into a 580 km x 610 km, sun-synchronous orbit with nominal 1 am and 1 pm equatorial crossing times. The Air Force Space Test Program provided the Orbital Sciences Taurus launch vehicle. The satellite has a design lifetime of a year, with the goal of three years. The satellite and payload can typically observe six sites per day, with either one or two observations per site from nadir and off-nadir angles. Data are stored in the satellite memory and down-linked to a ground station at Sandia National Laboratory. Data are then forwarded to the Data Processing and Analysis Center at Los Alamos National Laboratory for processing, analysis and distribution to the MTI team and collaborators. We will provide an overview of the Project, a few examples of data products, and an introduction to more detailed presentations in this special session.

  3. Monitoring urban growth by using segmentation-classification of multispectral Landsat images in Izmit, Turkey.

    PubMed

    Yildiz, Selin; Doker, Mehmet Fatih

    2016-07-01

    Assessing the spatial land use and land cover (LULC) information is essential for decision making and management of landscapes. In fact, LULC information has been changed dramatically in fast-growing cities. This results in wrong land use problems due to unplanned and uncontrolled urbanization. The planning and evaluating of limited natural resources under the pressure of a growing population can be possible when a precise land use management plan is established. Therefore, it is imperative to monitor continuous LULC changes for future planning. Remote sensing (RS) technique is used for determining changes in LULC in urban areas. In this study, we have focused on Izmit, which is one of a growing number of metropolitan cities where the impact of the spatial growing period on LULC has been assessed over the past 30 years by using RS data. We have utilized the segmentation process and supervised classification of Landsat satellite images for four different dates (1985, 1995, 2005, and 2015). The outcome of this research can be summarized by significant changes in the shares of urban areas and farmland LULC classes. The overall observed increase in urban area class is up to 2177 ha between 1985 and 2015 period and this dramatic change has resulted in the decline of 1211 ha of farmland. Another conclusion is that the new residential areas have been created to the north, south and east of Izmit during this period.

  4. Monitoring urban growth by using segmentation-classification of multispectral Landsat images in Izmit, Turkey.

    PubMed

    Yildiz, Selin; Doker, Mehmet Fatih

    2016-07-01

    Assessing the spatial land use and land cover (LULC) information is essential for decision making and management of landscapes. In fact, LULC information has been changed dramatically in fast-growing cities. This results in wrong land use problems due to unplanned and uncontrolled urbanization. The planning and evaluating of limited natural resources under the pressure of a growing population can be possible when a precise land use management plan is established. Therefore, it is imperative to monitor continuous LULC changes for future planning. Remote sensing (RS) technique is used for determining changes in LULC in urban areas. In this study, we have focused on Izmit, which is one of a growing number of metropolitan cities where the impact of the spatial growing period on LULC has been assessed over the past 30 years by using RS data. We have utilized the segmentation process and supervised classification of Landsat satellite images for four different dates (1985, 1995, 2005, and 2015). The outcome of this research can be summarized by significant changes in the shares of urban areas and farmland LULC classes. The overall observed increase in urban area class is up to 2177 ha between 1985 and 2015 period and this dramatic change has resulted in the decline of 1211 ha of farmland. Another conclusion is that the new residential areas have been created to the north, south and east of Izmit during this period. PMID:27270481

  5. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2016-03-01

    The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

  6. Unsupervised classification of remote multispectral sensing data

    NASA Technical Reports Server (NTRS)

    Su, M. Y.

    1972-01-01

    The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

  7. Multispectral imaging radar

    NASA Technical Reports Server (NTRS)

    Porcello, L. J.; Rendleman, R. A.

    1972-01-01

    A side-looking radar, installed in a C-46 aircraft, was modified to provide it with an initial multispectral imaging capability. The radar is capable of radiating at either of two wavelengths, these being approximately 3 cm and 30 cm, with either horizontal or vertical polarization on each wavelength. Both the horizontally- and vertically-polarized components of the reflected signal can be observed for each wavelength/polarization transmitter configuration. At present, two-wavelength observation of a terrain region can be accomplished within the same day, but not with truly simultaneous observation on both wavelengths. A multiplex circuit to permit this simultaneous observation has been designed. A brief description of the modified radar system and its operating parameters is presented. Emphasis is then placed on initial flight test data and preliminary interpretation. Some considerations pertinent to the calibration of such radars are presented in passing.

  8. A novel classification method for multispectral imaging combined with portable Raman spectroscopy for the analysis of a painting by Vincent Van Gogh.

    PubMed

    Cesaratto, Anna; Nevin, Austin; Valentini, Gianluca; Brambilla, Luigi; Castiglioni, Chiara; Toniolo, Lucia; Fratelli, Maria; Comelli, Daniela

    2013-11-01

    In this work, a novel combination of portable micro-Raman spectroscopy and semi-automatic methods of data treatment are proposed for the classification and mapping of visible multispectral imaging data for the analysis of a painting on paper by Vincent Van Gogh. Analysis of multispectral imaging data with the sequential maximum-angle convex cone (SMACC) and spectral angle mapper (SAM) algorithms differentiated the surface into areas on the basis of the presence of pigment mixtures. Complementary analytical information was obtained through portable Raman spectroscopy was performed on a few selected points of the painting, allowing for the determination of Van Gogh's palette and the mapping of pigment mixtures on the painting's surface; the number of mixtures employed is varied and at least two different blues are present. The results obtained were integrated with the information from prior ultraviolet (UV)-induced luminescence analysis performed on the same painting to better understand the materials used by the artist. The mathematical treatment of multispectral data using the proposed methods could be extended to the analysis of other painted surfaces.

  9. Multispectral Landsat images of Antartica

    SciTech Connect

    Lucchitta, B.K.; Bowell, J.A.; Edwards, K.L.; Eliason, E.M.; Fergurson, H.M.

    1988-01-01

    The U.S. Geological Survey has a program to map Antarctica by using colored, digitally enhanced Landsat multispectral scanner images to increase existing map coverage and to improve upon previously published Landsat maps. This report is a compilation of images and image mosaic that covers four complete and two partial 1:250,000-scale quadrangles of the McMurdo Sound region.

  10. Perspective of inline control of latent defects and diseases on french fries with multispectral imaging

    NASA Astrophysics Data System (ADS)

    Noordam, Jacco C.; van den Broek, Willie H.; Buydens, Lutgarde M.

    2004-03-01

    In this paper, the feasibility is investigated to improve discrimination between different defect and diseases on raw French fries with multispectral imaging. Four different potato cultivars are selected from which French Fries are cut. Both multispectral images and RGB color images are classified with linear Bayes normal classifier and a support vector classifier. The effect of applying different preprocessing techniques on the spectra prior to classification was also investigated. The classification result are compared with both RGB images and the full spectra classification results. Experimental results indicate that the support vector classifier gives the best performance for both multispectral and RGB color images and is less preprocessing dependent. The multispectral image classification results outperform the RGB color classification results with a factor 15 at best. An explorative multispectral analysis also shows that latent defects can be detected with multispectral imaging, in contrast with traditional color imaging.

  11. Multispectral imaging method and apparatus

    DOEpatents

    Sandison, D.R.; Platzbecker, M.R.; Vargo, T.D.; Lockhart, R.R.; Descour, M.R.; Richards-Kortum, R.

    1999-07-06

    A multispectral imaging method and apparatus are described which are adapted for use in determining material properties, especially properties characteristic of abnormal non-dermal cells. A target is illuminated with a narrow band light beam. The target expresses light in response to the excitation. The expressed light is collected and the target's response at specific response wavelengths to specific excitation wavelengths is measured. From the measured multispectral response the target's properties can be determined. A sealed, remote probe and robust components can be used for cervical imaging. 5 figs.

  12. Multispectral imaging method and apparatus

    DOEpatents

    Sandison, David R.; Platzbecker, Mark R.; Vargo, Timothy D.; Lockhart, Randal R.; Descour, Michael R.; Richards-Kortum, Rebecca

    1999-01-01

    A multispectral imaging method and apparatus adapted for use in determining material properties, especially properties characteristic of abnormal non-dermal cells. A target is illuminated with a narrow band light beam. The target expresses light in response to the excitation. The expressed light is collected and the target's response at specific response wavelengths to specific excitation wavelengths is measured. From the measured multispectral response the target's properties can be determined. A sealed, remote probe and robust components can be used for cervical imaging

  13. Non-destructive determination of total polyphenols content and classification of storage periods of Iron Buddha tea using multispectral imaging system.

    PubMed

    Xiong, Chuanwu; Liu, Changhong; Pan, Wenjuan; Ma, Fei; Xiong, Can; Qi, Li; Chen, Feng; Lu, Xuzhong; Yang, Jianbo; Zheng, Lei

    2015-06-01

    Total polyphenols is a primary quality indicator in tea which is consumed worldwide. The feasibility of using near infrared reflectance (NIR) spectroscopy (800-2500nm) and multispectral imaging (MSI) system (405-970nm) for prediction of total polyphenols contents (TPC) of Iron Buddha tea was investigated in this study. The results revealed that the predictive model by MSI using partial least squares (PLS) analysis for tea leaves was considered to be the best in non-destructive and rapid determination of TPC. Besides, the ability of MSI to classify tea leaves based on storage period (year of 2004, 2007, 2011, 2012 and 2013) was tested and the classification accuracies of 95.0% and 97.5% were achieved using LS-SVM and BPNN models, respectively. These overall results suggested that MSI together with suitable analysis model is a promising technology for rapid and non-destructive determination of TPC and classification of storage periods in tea leaves.

  14. Multispectral scanner imagery for plant community classification.

    NASA Technical Reports Server (NTRS)

    Driscoll, R. S.; Spencer, M. M.

    1973-01-01

    Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information for computerized classification of 11 plant communities and two nonvegetation classes. Intensive preprocessing of the spectral data was required to eliminate bidirectional reflectance effects of the spectral imagery caused by scanner view angle and varying geometry of the plant canopy. Generalized plant community types - forest, grassland, and hydrophytic systems - were acceptably classified based on ecological analysis. Serious, but soluble, errors occurred with attempts to classify specific community types within the grassland system. However, special clustering analyses provided for improved classification of specific grassland communities.

  15. Multispectral tissue analysis and classification towards enabling automated robotic surgery

    NASA Astrophysics Data System (ADS)

    Triana, Brian; Cha, Jaepyeong; Shademan, Azad; Krieger, Axel; Kang, Jin U.; Kim, Peter C. W.

    2014-02-01

    Accurate optical characterization of different tissue types is an important tool for potentially guiding surgeons and enabling automated robotic surgery. Multispectral imaging and analysis have been used in the literature to detect spectral variations in tissue reflectance that may be visible to the naked eye. Using this technique, hidden structures can be visualized and analyzed for effective tissue classification. Here, we investigated the feasibility of automated tissue classification using multispectral tissue analysis. Broadband reflectance spectra (200-1050 nm) were collected from nine different ex vivo porcine tissues types using an optical fiber-probe based spectrometer system. We created a mathematical model to train and distinguish different tissue types based upon analysis of the observed spectra using total principal component regression (TPCR). Compared to other reported methods, our technique is computationally inexpensive and suitable for real-time implementation. Each of the 92 spectra was cross-referenced against the nine tissue types. Preliminary results show a mean detection rate of 91.3%, with detection rates of 100% and 70.0% (inner and outer kidney), 100% and 100% (inner and outer liver), 100% (outer stomach), and 90.9%, 100%, 70.0%, 85.7% (four different inner stomach areas, respectively). We conclude that automated tissue differentiation using our multispectral tissue analysis method is feasible in multiple ex vivo tissue specimens. Although measurements were performed using ex vivo tissues, these results suggest that real-time, in vivo tissue identification during surgery may be possible.

  16. Gimbaled multispectral imaging system and method

    DOEpatents

    Brown, Kevin H.; Crollett, Seferino; Henson, Tammy D.; Napier, Matthew; Stromberg, Peter G.

    2016-01-26

    A gimbaled multispectral imaging system and method is described herein. In an general embodiment, the gimbaled multispectral imaging system has a cross support that defines a first gimbal axis and a second gimbal axis, wherein the cross support is rotatable about the first gimbal axis. The gimbaled multispectral imaging system comprises a telescope that fixed to an upper end of the cross support, such that rotation of the cross support about the first gimbal axis causes the tilt of the telescope to alter. The gimbaled multispectral imaging system includes optics that facilitate on-gimbal detection of visible light and off-gimbal detection of infrared light.

  17. Multispectral data acquisition and classification - Statistical models for system design

    NASA Technical Reports Server (NTRS)

    Huck, F. O.; Park, S. K.

    1978-01-01

    In this paper we relate the statistical processes that are involved in multispectral data acquisition and classification to a simple radiometric model of the earth surface and atmosphere. If generalized, these formulations could provide an analytical link between the steadily improving models of our environment and the performance characteristics of rapidly advancing device technology. This link is needed to bring system analysis tools to the task of optimizing remote sensing and (real-time) signal processing systems as a function of target and atmospheric properties, remote sensor spectral bands and system topology (e.g., image-plane processing), radiometric sensitivity and calibration accuracy, compensation for imaging conditions (e.g., atmospheric effects), and classification rates and errors.

  18. Multispectral Image Processing for Plants

    NASA Technical Reports Server (NTRS)

    Miles, Gaines E.

    1991-01-01

    The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

  19. Galileo multispectral imaging of Earth.

    PubMed

    Geissler, P; Thompson, W R; Greenberg, R; Moersch, J; McEwen, A; Sagan, C

    1995-08-25

    Nearly 6000 multispectral images of Earth were acquired by the Galileo spacecraft during its two flybys. The Galileo images offer a unique perspective on our home planet through the spectral capability made possible by four narrowband near-infrared filters, intended for observations of methane in Jupiter's atmosphere, which are not incorporated in any of the currently operating Earth orbital remote sensing systems. Spectral variations due to mineralogy, vegetative cover, and condensed water are effectively mapped by the visible and near-infrared multispectral imagery, showing a wide variety of biological, meteorological, and geological phenomena. Global tectonic and volcanic processes are clearly illustrated by these images, providing a useful basis for comparative planetary geology. Differences between plant species are detected through the narrowband IR filters on Galileo, allowing regional measurements of variation in the "red edge" of chlorophyll and the depth of the 1-micrometer water band, which is diagnostic of leaf moisture content. Although evidence of life is widespread in the Galileo data set, only a single image (at approximately 2 km/pixel) shows geometrization plausibly attributable to our technical civilization. Water vapor can be uniquely imaged in the Galileo 0.73-micrometer band, permitting spectral discrimination of moist and dry clouds with otherwise similar albedo. Surface snow and ice can be readily distinguished from cloud cover by narrowband imaging within the sensitivity range of Galileo's silicon CCD camera. Ice grain size variations can be mapped using the weak H2O absorption at 1 micrometer, a technique which may find important applications in the exploration of the moons of Jupiter. The Galileo images have the potential to make unique contributions to Earth science in the areas of geological, meteorological and biological remote sensing, due to the inclusion of previously untried narrowband IR filters. The vast scale and near global

  20. Galileo multispectral imaging of Earth.

    PubMed

    Geissler, P; Thompson, W R; Greenberg, R; Moersch, J; McEwen, A; Sagan, C

    1995-08-25

    Nearly 6000 multispectral images of Earth were acquired by the Galileo spacecraft during its two flybys. The Galileo images offer a unique perspective on our home planet through the spectral capability made possible by four narrowband near-infrared filters, intended for observations of methane in Jupiter's atmosphere, which are not incorporated in any of the currently operating Earth orbital remote sensing systems. Spectral variations due to mineralogy, vegetative cover, and condensed water are effectively mapped by the visible and near-infrared multispectral imagery, showing a wide variety of biological, meteorological, and geological phenomena. Global tectonic and volcanic processes are clearly illustrated by these images, providing a useful basis for comparative planetary geology. Differences between plant species are detected through the narrowband IR filters on Galileo, allowing regional measurements of variation in the "red edge" of chlorophyll and the depth of the 1-micrometer water band, which is diagnostic of leaf moisture content. Although evidence of life is widespread in the Galileo data set, only a single image (at approximately 2 km/pixel) shows geometrization plausibly attributable to our technical civilization. Water vapor can be uniquely imaged in the Galileo 0.73-micrometer band, permitting spectral discrimination of moist and dry clouds with otherwise similar albedo. Surface snow and ice can be readily distinguished from cloud cover by narrowband imaging within the sensitivity range of Galileo's silicon CCD camera. Ice grain size variations can be mapped using the weak H2O absorption at 1 micrometer, a technique which may find important applications in the exploration of the moons of Jupiter. The Galileo images have the potential to make unique contributions to Earth science in the areas of geological, meteorological and biological remote sensing, due to the inclusion of previously untried narrowband IR filters. The vast scale and near global

  1. Hierarchical Object-based Image Analysis approach for classification of sub-meter multispectral imagery in Tanzania

    NASA Astrophysics Data System (ADS)

    Chung, C.; Nagol, J. R.; Tao, X.; Anand, A.; Dempewolf, J.

    2015-12-01

    Increasing agricultural production while at the same time preserving the environment has become a challenging task. There is a need for new approaches for use of multi-scale and multi-source remote sensing data as well as ground based measurements for mapping and monitoring crop and ecosystem state to support decision making by governmental and non-governmental organizations for sustainable agricultural development. High resolution sub-meter imagery plays an important role in such an integrative framework of landscape monitoring. It helps link the ground based data to more easily available coarser resolution data, facilitating calibration and validation of derived remote sensing products. Here we present a hierarchical Object Based Image Analysis (OBIA) approach to classify sub-meter imagery. The primary reason for choosing OBIA is to accommodate pixel sizes smaller than the object or class of interest. Especially in non-homogeneous savannah regions of Tanzania, this is an important concern and the traditional pixel based spectral signature approach often fails. Ortho-rectified, calibrated, pan sharpened 0.5 meter resolution data acquired from DigitalGlobe's WorldView-2 satellite sensor was used for this purpose. Multi-scale hierarchical segmentation was performed using multi-resolution segmentation approach to facilitate the use of texture, neighborhood context, and the relationship between super and sub objects for training and classification. eCognition, a commonly used OBIA software program, was used for this purpose. Both decision tree and random forest approaches for classification were tested. The Kappa index agreement for both algorithms surpassed the 85%. The results demonstrate that using hierarchical OBIA can effectively and accurately discriminate classes at even LCCS-3 legend.

  2. 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.

  3. Multispectral imaging with vertical silicon nanowires

    PubMed Central

    Park, Hyunsung; Crozier, Kenneth B.

    2013-01-01

    Multispectral imaging is a powerful tool that extends the capabilities of the human eye. However, multispectral imaging systems generally are expensive and bulky, and multiple exposures are needed. Here, we report the demonstration of a compact multispectral imaging system that uses vertical silicon nanowires to realize a filter array. Multiple filter functions covering visible to near-infrared (NIR) wavelengths are simultaneously defined in a single lithography step using a single material (silicon). Nanowires are then etched and embedded into polydimethylsiloxane (PDMS), thereby realizing a device with eight filter functions. By attaching it to a monochrome silicon image sensor, we successfully realize an all-silicon multispectral imaging system. We demonstrate visible and NIR imaging. We show that the latter is highly sensitive to vegetation and furthermore enables imaging through objects opaque to the eye. PMID:23955156

  4. SWNT Imaging Using Multispectral Image Processing

    NASA Astrophysics Data System (ADS)

    Blades, Michael; Pirbhai, Massooma; Rotkin, Slava V.

    2012-02-01

    A flexible optical system was developed to image carbon single-wall nanotube (SWNT) photoluminescence using the multispectral capabilities of a typical CCD camcorder. The built in Bayer filter of the CCD camera was utilized, using OpenCV C++ libraries for image processing, to decompose the image generated in a high magnification epifluorescence microscope setup into three pseudo-color channels. By carefully calibrating the filter beforehand, it was possible to extract spectral data from these channels, and effectively isolate the SWNT signals from the background.

  5. Multispectral Imaging from Mars PATHFINDER

    NASA Technical Reports Server (NTRS)

    Ferrand, William H.; Bell, James F., III; Johnson, Jeffrey R.; Bishop, Janice L.; Morris, Richard V.

    2007-01-01

    The Imager for Mars Pathfinder (IMP) was a mast-mounted instrument on the Mars Pathfinder lander which landed on Mars Ares Vallis floodplain on July 4, 1997. During the 83 sols of Mars Pathfinders landed operations, the IMP collected over 16,600 images. Multispectral images were collected using twelve narrowband filters at wavelengths between 400 and 1000 nm in the visible and near infrared (VNIR) range. The IMP provided VNIR spectra of the materials surrounding the lander including rocks, bright soils, dark soils, and atmospheric observations. During the primary mission, only a single primary rock spectral class, Gray Rock, was recognized; since then, Black Rock, has been identified. The Black Rock spectra have a stronger absorption at longer wavelengths than do Gray Rock spectra. A number of coated rocks have also been described, the Red and Maroon Rock classes, and perhaps indurated soils in the form of the Pink Rock class. A number of different soil types were also recognized with the primary ones being Bright Red Drift, Dark Soil, Brown Soil, and Disturbed Soil. Examination of spectral parameter plots indicated two trends which were interpreted as representing alteration products formed in at least two different environmental epochs of the Ares Vallis area. Subsequent analysis of the data and comparison with terrestrial analogs have supported the interpretation that the rock coatings provide evidence of earlier martian environments. However, the presence of relatively uncoated examples of the Gray and Black rock classes indicate that relatively unweathered materials can persist on the martian surface.

  6. Multispectral Imaging Broadens Cellular Analysis

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Amnis Corporation, a Seattle-based biotechnology company, developed ImageStream to produce sensitive fluorescence images of cells in flow. The company responded to an SBIR solicitation from Ames Research Center, and proposed to evaluate several methods of extending the depth of field for its ImageStream system and implement the best as an upgrade to its commercial products. This would allow users to view whole cells at the same time, rather than just one section of each cell. Through Phase I and II SBIR contracts, Ames provided Amnis the funding the company needed to develop this extended functionality. For NASA, the resulting high-speed image flow cytometry process made its way into Medusa, a life-detection instrument built to collect, store, and analyze sample organisms from erupting hydrothermal vents, and has the potential to benefit space flight health monitoring. On the commercial end, Amnis has implemented the process in ImageStream, combining high-resolution microscopy and flow cytometry in a single instrument, giving researchers the power to conduct quantitative analyses of individual cells and cell populations at the same time, in the same experiment. ImageStream is also built for many other applications, including cell signaling and pathway analysis; classification and characterization of peripheral blood mononuclear cell populations; quantitative morphology; apoptosis (cell death) assays; gene expression analysis; analysis of cell conjugates; molecular distribution; and receptor mapping and distribution.

  7. A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

    PubMed Central

    Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

    2016-01-01

    The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. PMID:27376088

  8. A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data.

    PubMed

    Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

    2016-01-01

    The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. PMID:27376088

  9. A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data.

    PubMed

    Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

    2016-01-01

    The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively.

  10. Semi-supervised classification tool for DubaiSat-2 multispectral imagery

    NASA Astrophysics Data System (ADS)

    Al-Mansoori, Saeed

    2015-10-01

    This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

  11. High-speed multispectral confocal imaging

    NASA Astrophysics Data System (ADS)

    Carver, Gary E.; Locknar, Sarah A.; Morrison, William A.; Farkas, Daniel L.

    2013-02-01

    A new approach for generating high-speed multispectral images has been developed. The central concept is that spectra can be acquired for each pixel in a confocal spatial scan by using a fast spectrometer based on optical fiber delay lines. This concept merges fast spectroscopy with standard spatial scanning to create datacubes in real time. The spectrometer is based on a serial array of reflecting spectral elements, delay lines between these elements, and a single element detector. The spatial, spectral, and temporal resolution of the instrument is described, and illustrated by multispectral images of laser-induced autofluorescence in biological tissues.

  12. High-speed multispectral confocal biomedical imaging

    PubMed Central

    Carver, Gary E.; Locknar, Sarah A.; Morrison, William A.; Krishnan Ramanujan, V.; Farkas, Daniel L.

    2014-01-01

    Abstract. A new approach for generating high-speed multispectral confocal images has been developed. The central concept is that spectra can be acquired for each pixel in a confocal spatial scan by using a fast spectrometer based on optical fiber delay lines. This approach merges fast spectroscopy with standard spatial scanning to create datacubes in real time. The spectrometer is based on a serial array of reflecting spectral elements, delay lines between these elements, and a single element detector. The spatial, spectral, and temporal resolution of the instrument is described and illustrated by multispectral images of laser-induced autofluorescence in biological tissues. PMID:24658777

  13. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    DOE PAGES

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

  14. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    SciTech Connect

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  15. Multispectral imaging using a single bucket detector

    PubMed Central

    Bian, Liheng; Suo, Jinli; Situ, Guohai; Li, Ziwei; Fan, Jingtao; Chen, Feng; Dai, Qionghai

    2016-01-01

    Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging using a single bucket detector, to take full advantage of its high sensitivity, wide spectrum range, low cost, small size and light weight. Technically, utilizing the detector’s fast response, a scene’s 3D spatial-spectral information is multiplexed into a dense 1D measurement sequence and then demultiplexed computationally under the single pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 64 pixels × 64 pixels × 10 wavelength bands ranging from 450 nm to 650 nm, with the acquisition time being 1 minute. The imaging scheme holds great potentials for various low light and airborne applications, and can be easily manufactured as production-volume portable multispectral imagers. PMID:27103168

  16. Multispectral imaging using a single bucket detector

    NASA Astrophysics Data System (ADS)

    Bian, Liheng; Suo, Jinli; Situ, Guohai; Li, Ziwei; Fan, Jingtao; Chen, Feng; Dai, Qionghai

    2016-04-01

    Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging using a single bucket detector, to take full advantage of its high sensitivity, wide spectrum range, low cost, small size and light weight. Technically, utilizing the detector’s fast response, a scene’s 3D spatial-spectral information is multiplexed into a dense 1D measurement sequence and then demultiplexed computationally under the single pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 64 pixels × 64 pixels × 10 wavelength bands ranging from 450 nm to 650 nm, with the acquisition time being 1 minute. The imaging scheme holds great potentials for various low light and airborne applications, and can be easily manufactured as production-volume portable multispectral imagers.

  17. Multispectral laser imaging for advanced food analysis

    NASA Astrophysics Data System (ADS)

    Senni, L.; Burrascano, P.; Ricci, M.

    2016-07-01

    A hardware-software apparatus for food inspection capable of realizing multispectral NIR laser imaging at four different wavelengths is herein discussed. The system was designed to operate in a through-transmission configuration to detect the presence of unwanted foreign bodies inside samples, whether packed or unpacked. A modified Lock-In technique was employed to counterbalance the significant signal intensity attenuation due to transmission across the sample and to extract the multispectral information more efficiently. The NIR laser wavelengths used to acquire the multispectral images can be varied to deal with different materials and to focus on specific aspects. In the present work the wavelengths were selected after a preliminary analysis to enhance the image contrast between foreign bodies and food in the sample, thus identifying the location and nature of the defects. Experimental results obtained from several specimens, with and without packaging, are presented and the multispectral image processing as well as the achievable spatial resolution of the system are discussed.

  18. Testing of Land Cover Classification from Multispectral Airborne Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Bakuła, K.; Kupidura, P.; Jełowicki, Ł.

    2016-06-01

    Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return

  19. Digital preprocessing and classification of multispectral earth observation data

    NASA Technical Reports Server (NTRS)

    Anuta, P. E.

    1976-01-01

    The development of airborne and satellite multispectral image scanning sensors has generated wide-spread interest in application of these sensors to earth resource mapping. These point scanning sensors permit scenes to be imaged in a large number of electromagnetic energy bands between .3 and 15 micrometers. The energy sensed in each band can be used as a feature in a computer based multi-dimensional pattern recognition process to aid in interpreting the nature of elements in the scene. Images from each band can also be interpreted visually. Visual interpretation of five or ten multispectral images simultaneously becomes impractical especially as area studied increases; hence, great emphasis has been placed on machine (computer) techniques for aiding in the interpretation process. This paper describes a computer software system concept called LARSYS for analysis of multivariate image data and presents some examples of its application.

  20. Multi-spectral imaging with mid-infrared semiconductor lasers

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Wang, Yang; Le, Han Q.

    2006-01-01

    Multi-spectral laser imaging can be a useful technology for target discrimination, classification, and identification based on object spectral signatures. The mid-IR region (~3-14 μm) is particularly rich of molecular spectroscopic fingerprints, but the technology has been under utilized. Compact, potentially inexpensive semiconductor lasers may allow more cost-effective applications. This paper describes a development of semiconductor-laser-based multi-spectral imaging for both near-IR and mid-IR, and demonstrates the potential of this technology. The near-IR study employed 7 wavelengths from 0.635-1.55 μm, and used for system engineering evaluation as well as for studying the fundamental aspects of multi-spectral laser imaging. These include issues of wavelength-dependence scattering as a function of incident and receiving angle and the polarization effects. Stokes vector imaging and degree-of-linear-polarization were shown to reveal significant information to characterize the targets. The mid-IR study employed 4 wavelengths from 3.3-9.6 μm, and was applied to diverse targets that consist of natural and man-made materials and household objects. It was shown capable to resolve and distinguish small spectral differences among various targets, thanks to the laser radiometric and spectral accuracy. Colorless objects in the visible were shown with "colorful" signatures in the mid-IR. An essential feature of the study is an advanced system architecture that employs wavelength-division-multiplexed laser beams for high spectral fidelity and resolution. In addition, unlike conventional one-transmitter and one receiver design, the system is based on a scalable CDMA network concept with multiple transmitters and receivers to allow efficient information acquisition. The results suggest that multi-spectral laser imaging in general can be a unique and powerful technology for wide ranging applications.

  1. Image processing of underwater multispectral imagery

    USGS Publications Warehouse

    Zawada, D.G.

    2003-01-01

    Capturing in situ fluorescence images of marine organisms presents many technical challenges. The effects of the medium, as well as the particles and organisms within it, are intermixed with the desired signal. Methods for extracting and preparing the imagery for analysis are discussed in reference to a novel underwater imaging system called the low-light-level underwater multispectral imaging system (LUMIS). The instrument supports both uni- and multispectral collections, each of which is discussed in the context of an experimental application. In unispectral mode, LUMIS was used to investigate the spatial distribution of phytoplankton. A thin sheet of laser light (532 nm) induced chlorophyll fluorescence in the phytoplankton, which was recorded by LUMIS. Inhomogeneities in the light sheet led to the development of a beam-pattern-correction algorithm. Separating individual phytoplankton cells from a weak background fluorescence field required a two-step procedure consisting of edge detection followed by a series of binary morphological operations. In multispectral mode, LUMIS was used to investigate the bio-assay potential of fluorescent pigments in corals. Problems with the commercial optical-splitting device produced nonlinear distortions in the imagery. A tessellation algorithm, including an automated tie-point-selection procedure, was developed to correct the distortions. Only pixels corresponding to coral polyps were of interest for further analysis. Extraction of these pixels was performed by a dynamic global-thresholding algorithm.

  2. Multispectral imaging for diagnosis and treatment

    NASA Astrophysics Data System (ADS)

    Carver, Gary E.; Locknar, Sarah A.; Morrison, William A.; Farkas, Daniel L.

    2014-03-01

    A new approach for generating high-speed multispectral images has been previously reported by our team. The central concept is that spectra can be acquired for each pixel in a confocal spatial laser scan by using a fast spectrometer based on optical fiber delay lines. This method merges fast spectroscopy with standard spatial scanning to create image datacubes in real time. The datacubes can be analyzed to define regions of interest (ROIs) containing diseased tissue. Firmware and software have been developed for selectively scanning these ROIs with increased optical power. This enables real time image-guided laser treatment with a spatial resolution of a few microns.

  3. Reproducible high-resolution multispectral image acquisition in dermatology

    NASA Astrophysics Data System (ADS)

    Duliu, Alexandru; Gardiazabal, José; Lasser, Tobias; Navab, Nassir

    2015-07-01

    Multispectral image acquisitions are increasingly popular in dermatology, due to their improved spectral resolution which enables better tissue discrimination. Most applications however focus on restricted regions of interest, imaging only small lesions. In this work we present and discuss an imaging framework for high-resolution multispectral imaging on large regions of interest.

  4. Simultaneous multispectral imaging using lenslet arrays

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele; Jensen, James

    2013-03-01

    There is a need for small compact multispectral and hyperspectral imaging systems that simultaneously images in many spectral bands across the infrared spectral region from short to long-wave infrared. This is a challenge for conventional optics and usually requires large, costly and complex optical systems. However, with the advances in materials and photolithographic technology, Micro-Optical-Electrical-Machine-Systems (MOEMS) can meet these goals. In this paper Pacific Advanced Technology and ECBC will present the work that we are doing under a SBIR contract to the US Army using a MOEMS based diffractive optical lenslet array to perform simultaneous multispectral and hyperspectral imaging with relatively high spatial resolution. Under this program we will develop a proof of concept system that demonstrates how a diffractive optical (DO) lenslet array can image 1024 x 1024 pixels in 16 colors every frame of the camera. Each color image has a spatial resolution of 256 x 256 pixels with an IFOV of 1.7 mrads and FOV of 25 degrees. The purpose of this work is to simultaneously image multiple colors each frame and reduce the temporal changes between colors that are apparent in sequential multispectral imaging. Translating the lenslet array will collect hyperspectral image data cubes as will be explained later in this paper. Because the optics is integrated with the detector the entire multispectral/hyperspectral system can be contained in a miniature package. The spectral images are collected simultaneously allowing high resolution spectral-spatial-temporal information each frame of the camera. Thus enabling the implementation of spectral-temporal-spatial algorithms in real-time with high sensitivity for the detection of weak signals in a high background clutter environment with low sensitivity to camera motion. Using MOEMS actuation the DO lenslet array is translated along the optical axis to complete the full hyperspectral data cube in just a few frames of the

  5. Multispectral multisensor image fusion using wavelet transforms

    USGS Publications Warehouse

    Lemeshewsky, George P.

    1999-01-01

    Fusion techniques can be applied to multispectral and higher spatial resolution panchromatic images to create a composite image that is easier to interpret than the individual images. Wavelet transform-based multisensor, multiresolution fusion (a type of band sharpening) was applied to Landsat thematic mapper (TM) multispectral and coregistered higher resolution SPOT panchromatic images. The objective was to obtain increased spatial resolution, false color composite products to support the interpretation of land cover types wherein the spectral characteristics of the imagery are preserved to provide the spectral clues needed for interpretation. Since the fusion process should not introduce artifacts, a shift invariant implementation of the discrete wavelet transform (SIDWT) was used. These results were compared with those using the shift variant, discrete wavelet transform (DWT). Overall, the process includes a hue, saturation, and value color space transform to minimize color changes, and a reported point-wise maximum selection rule to combine transform coefficients. The performance of fusion based on the SIDWT and DWT was evaluated with a simulated TM 30-m spatial resolution test image and a higher resolution reference. Simulated imagery was made by blurring higher resolution color-infrared photography with the TM sensors' point spread function. The SIDWT based technique produced imagery with fewer artifacts and lower error between fused images and the full resolution reference. Image examples with TM and SPOT 10-m panchromatic illustrate the reduction in artifacts due to the SIDWT based fusion.

  6. Lossless compression algorithm for multispectral imagers

    NASA Astrophysics Data System (ADS)

    Gladkova, Irina; Grossberg, Michael; Gottipati, Srikanth

    2008-08-01

    Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements from a scene across space and spectrum. Growing data rates resulting from faster scanning and finer spatial and spectral resolution makes compression an increasingly critical tool to reduce data volume for transmission and archiving. Research for NOAA NESDIS has been directed to finding for the characteristics of satellite atmospheric Earth science Imager sensor data what level of Lossless compression ratio can be obtained as well as appropriate types of mathematics and approaches that can lead to approaching this data's entropy level. Conventional lossless do not achieve the theoretical limits for lossless compression on imager data as estimated from the Shannon entropy. In a previous paper, the authors introduce a lossless compression algorithm developed for MODIS as a proxy for future NOAA-NESDIS satellite based Earth science multispectral imagers such as GOES-R. The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations with a linear transform encoder. In decompression, the algorithm uses a statistically computed look up table to iteratively predict each channel from a channel decompressed in the previous iteration. In this paper we present a new approach which fundamentally differs from our prior work. In this new approach, instead of having a single predictor for each pair of bands we introduce a piecewise spatially varying predictor which significantly improves the compression results. Our new algorithm also now optimizes the sequence of channels we use for prediction. Our results are evaluated by comparison with a state of the art wavelet based image compression scheme, Jpeg2000. We present results on the 14 channel subset of the MODIS imager, which serves as a proxy for the GOES-R imager. We

  7. Multispectral imaging for medical diagnosis

    NASA Technical Reports Server (NTRS)

    Anselmo, V. J.

    1977-01-01

    Photography technique determines amount of morbidity present in tissue. Imaging apparatus incorporates numerical filtering. Overall system operates in near-real time. Information gained from this system enables physician to understand extent of injury and leads to accelerated treatment.

  8. Multispectral Imaging Of Burn Wounds

    NASA Astrophysics Data System (ADS)

    Afromowitz, Martin A.; Callis, James B.; Heimbach, David M.; DeSoto, Larry A.; Norton, Mary K.

    1988-06-01

    This research program successfully developed a real-time video imaging system (the Imaging Burn Depth Indicator, or IBDI) which can discriminate areas of burn wounds expected to heal in three weeks or less from the day of injury from those areas not expected to heal in that time period. The analysis can be performed on or about the third day post-burn on debrided burn wounds. Early evaluation of burn healing probability is a crucial factor in the decision to tangentially excise the burn wound. The IBDI measures the reflectivity of the burn wound in the red, green, and near infrared wavelength bands, which data correlate with burn healing probability. The instrument uses an algorithm established in an earlier study to translate the optical data into burn healing probabilities. The IBDI produces two types of images: a true-color image of the burn and a false-color image of the burn. The false-color image consists of up to four colors, each of which indicates a distinct range of probability that the area of the burn so colored will heal within 21 days. Over 100 burn wound sites were studied. Burn sites were evaluated on day three post-burn by our instrument and by the attending physician. Of 55 sites considered to be of intermediate depth, the IBDI predicted the healing outcome accurately in 84% of the cases. By comparison, the predictions of burn surgeons supervising the care of these patients were accurate in 62% of the cases.

  9. The Multispectral Imaging Science Working Group. Volume 3: Appendices

    NASA Technical Reports Server (NTRS)

    Cox, S. C. (Editor)

    1982-01-01

    The status and technology requirements for using multispectral sensor imagery in geographic, hydrologic, and geologic applications are examined. Critical issues in image and information science are identified.

  10. SLIM for multispectral FRET imaging

    NASA Astrophysics Data System (ADS)

    Rück, A.; Dolp, F.; Steiner, R.; Steinmetz, C.; von Einem, B.; von Arnim, C. A. F.

    2008-02-01

    Spectral fluorescence lifetime imaging (SLIM) is an advanced imaging technique, which combines spectral with time resolved detection. Real spectral information is achieved by using a grating in front of a PML-array, which allows time-correlated single photon counting (TCSPC). Whereas spectrally resolved fluorescence imaging alone has a reasonable sensitivity, the specificity of fluorescence detection can be improved by considering the fluorescence lifetime. The various possibilities which SLIM offers to improve FRET (resonant energy transfer) will be discussed as well as successfully realized applications. These include FRET measurements for protein interactions, related to Alzheimer's disease. Special attention will be focused on molecules involved in the processing and trafficking of the amyloid precursor protein (APP), as trafficking proteins of the GGA family and β-secretase BACE). Taking into account also the lifetime of the acceptor could enhance reliability of the FRET result.

  11. Multispectral fingerprint imaging for spoof detection

    NASA Astrophysics Data System (ADS)

    Nixon, Kristin A.; Rowe, Robert K.

    2005-03-01

    Fingerprint systems are the most widespread form of biometric authentication. Used in locations such as airports and in PDA's and laptops, fingerprint readers are becoming more common in everyday use. As they become more familiar, the security weaknesses of fingerprint sensors are becoming better known. Numerous websites now exist describing in detail how to create a fake fingerprint usable for spoofing a biometric system from both a cooperative user and from latent prints. While many commercial fingerprint readers claim to have some degree of spoof detection incorporated, they are still generally susceptible to spoof attempts using various artificial fingerprint samples made from gelatin or silicone or other materials and methods commonly available on the web. This paper describes a multispectral sensor that has been developed to collect data for spoof detection. The sensor has been designed to work in conjunction with a conventional optical fingerprint reader such that all images are collected during a single placement of the finger on the sensor. The multispectral imaging device captures sub-surface information about the finger that makes it very difficult to spoof. Four attributes of the finger that are collected with the multispectral imager will be described and demonstrated in this paper: spectral qualities of live skin, chromatic texture of skin, sub-surface image of live skin, and blanching on contact. Each of these attributes is well suited to discriminating against particular kinds of spoofing samples. A series of experiments was conducted to demonstrate the capabilities of the individual attributes as well as the collective spoof detection performance.

  12. Multispectral imaging of aircraft exhaust

    NASA Astrophysics Data System (ADS)

    Berkson, Emily E.; Messinger, David W.

    2016-05-01

    Aircraft pollutants emitted during the landing-takeoff (LTO) cycle have significant effects on the local air quality surrounding airports. There are currently no inexpensive, portable, and unobtrusive sensors to quantify the amount of pollutants emitted from aircraft engines throughout the LTO cycle or to monitor the spatial-temporal extent of the exhaust plume. We seek to thoroughly characterize the unburned hydrocarbon (UHC) emissions from jet engine plumes and to design a portable imaging system to remotely quantify the emitted UHCs and temporally track the distribution of the plume. This paper shows results from the radiometric modeling of a jet engine exhaust plume and describes a prototype long-wave infrared imaging system capable of meeting the above requirements. The plume was modeled with vegetation and sky backgrounds, and filters were selected to maximize the detectivity of the plume. Initial calculations yield a look-up chart, which relates the minimum amount of emitted UHCs required to detect the presence of a plume to the noise-equivalent radiance of a system. Future work will aim to deploy the prototype imaging system at the Greater Rochester International Airport to assess the applicability of the system on a national scale. This project will help monitor the local pollution surrounding airports and allow better-informed decision-making regarding emission caps and pollution bylaws.

  13. Use of Multispectral Imaging in Varietal Identification of Tomato

    PubMed Central

    Shrestha, Santosh; Deleuran, Lise Christina; Olesen, Merete Halkjær; Gislum, René

    2015-01-01

    Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration. PMID:25690549

  14. Multi-spectral imaging of oxygen saturation

    NASA Astrophysics Data System (ADS)

    Savelieva, Tatiana A.; Stratonnikov, Aleksander A.; Loschenov, Victor B.

    2008-06-01

    The system of multi-spectral imaging of oxygen saturation is an instrument that can record both spectral and spatial information about a sample. In this project, the spectral imaging technique is used for monitoring of oxygen saturation of hemoglobin in human tissues. This system can be used for monitoring spatial distribution of oxygen saturation in photodynamic therapy, surgery or sports medicine. Diffuse reflectance spectroscopy in the visible range is an effective and extensively used technique for the non-invasive study and characterization of various biological tissues. In this article, a short review of modeling techniques being currently in use for diffuse reflection from semi-infinite turbid media is presented. A simple and practical model for use with a real-time imaging system is proposed. This model is based on linear approximation of the dependence of the diffuse reflectance coefficient on relation between absorbance and reduced scattering coefficient. This dependence was obtained with the Monte Carlo simulation of photon propagation in turbid media. Spectra of the oxygenated and deoxygenated forms of hemoglobin differ mostly in the red area (520 - 600 nm) and have several characteristic points there. Thus four band-pass filters were used for multi-spectral imaging. After having measured the reflectance, the data obtained are used for fitting the concentration of oxygenated and free hemoglobin, and hemoglobin oxygen saturation.

  15. Modeling misregistration and related effects on multispectral classification

    NASA Technical Reports Server (NTRS)

    Billingsley, F. C.

    1981-01-01

    The effects of misregistration on the multispectral classification accuracy when the scene registration accuracy is relaxed from 0.3 to 0.5 pixel are investigated. Noise, class separability, spatial transient response, and field size are considered simultaneously with misregistration in their effects on accuracy. Any noise due to the scene, sensor, or to the analog/digital conversion, causes a finite fraction of the measurements to fall outside of the classification limits, even within nominally uniform fields. Misregistration causes field borders in a given band or set of bands to be closer than expected to a given pixel, causing additional pixels to be misclassified due to the mixture of materials in the pixel. Simplified first order models of the various effects are presented, and are used to estimate the performance to be expected.

  16. Multi-spectral compressive snapshot imaging using RGB image sensors.

    PubMed

    Rueda, Hoover; Lau, Daniel; Arce, Gonzalo R

    2015-05-01

    Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color images from a single snapshot taken by a monochrome image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the image sensor pixels to account for color and then investigate the impact on image quality using either a traditional color image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon image sensor which stacks red, green, and blue pixels on top of one another. PMID:25969307

  17. Multispectral imaging system for contaminant detection

    NASA Technical Reports Server (NTRS)

    Poole, Gavin H. (Inventor)

    2003-01-01

    An automated inspection system for detecting digestive contaminants on food items as they are being processed for consumption includes a conveyor for transporting the food items, a light sealed enclosure which surrounds a portion of the conveyor, with a light source and a multispectral or hyperspectral digital imaging camera disposed within the enclosure. Operation of the conveyor, light source and camera are controlled by a central computer unit. Light reflected by the food items within the enclosure is detected in predetermined wavelength bands, and detected intensity values are analyzed to detect the presence of digestive contamination.

  18. Viability prediction of Ricinus cummunis L. seeds using multispectral imaging.

    PubMed

    Olesen, Merete Halkjær; Nikneshan, Pejman; Shrestha, Santosh; Tadayyon, Ali; Deleuran, Lise Christina; Boelt, Birte; Gislum, René

    2015-01-01

    The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375-970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour.

  19. Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

    PubMed Central

    Olesen, Merete Halkjær; Nikneshan, Pejman; Shrestha, Santosh; Tadayyon, Ali; Deleuran, Lise Christina; Boelt, Birte; Gislum, René

    2015-01-01

    The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour. PMID:25690554

  20. [Multispectral image compression algorithms for color reproduction].

    PubMed

    Liang, Wei; Zeng, Ping; Luo, Xue-mei; Wang, Yi-feng; Xie, Kun

    2015-01-01

    In order to improve multispectral images compression efficiency and further facilitate their storage and transmission for the application of color reproduction and so on, in which fields high color accuracy is desired, WF serial methods is proposed, and APWS_RA algorithm is designed. Then the WF_APWS_RA algorithm, which has advantages of low complexity, good illuminant stability and supporting consistent coior reproduction across devices, is presented. The conventional MSE based wavelet embedded coding principle is first studied. And then color perception distortion criterion and visual characteristic matrix W are proposed. Meanwhile, APWS_RA algorithm is formed by optimizing the. rate allocation strategy of APWS. Finally, combined above technologies, a new coding method named WF_APWS_RA is designed. Colorimetric error criterion is used in the algorithm and APWS_RA is applied on visual weighted multispectral image. In WF_APWS_RA, affinity propagation clustering is utilized to exploit spectral correlation of weighted image. Then two-dimensional wavelet transform is used to remove the spatial redundancy. Subsequently, error compensation mechanism and rate pre-allocation are combined to accomplish the embedded wavelet coding. Experimental results show that at the same bit rate, compared with classical coding algorithms, WF serial algorithms have better performance on color retention. APWS_RA preserves least spectral error and WF APWS_RA algorithm has obvious superiority on color accuracy.

  1. Extraction of topographic and spectral albedo information from multispectral images.

    USGS Publications Warehouse

    Eliason, P.T.; Soderblom, L.A.; Chavez, P.A., Jr.

    1981-01-01

    A technique has been developed to separate and extract spectral-reflectivity variations and topographic informaiton from multispectral images. The process is a completely closed system employing only the image data and can be applied to any digital multispectral data set. -from Authors

  2. Compressive hyperspectral and multispectral imaging fusion

    NASA Astrophysics Data System (ADS)

    Espitia, Óscar; Castillo, Sergio; Arguello, Henry

    2016-05-01

    Image fusion is a valuable framework which combines two or more images of the same scene from one or multiple sensors, allowing to improve the resolution of the images and increase the interpretable content. In remote sensing a common fusion problem consists of merging hyperspectral (HS) and multispectral (MS) images that involve large amount of redundant data, which ignores the highly correlated structure of the datacube along the spatial and spectral dimensions. Compressive HS and MS systems compress the spectral data in the acquisition step allowing to reduce the data redundancy by using different sampling patterns. This work presents a compressed HS and MS image fusion approach, which uses a high dimensional joint sparse model. The joint sparse model is formulated by combining HS and MS compressive acquisition models. The high spectral and spatial resolution image is reconstructed by using sparse optimization algorithms. Different fusion spectral image scenarios are used to explore the performance of the proposed scheme. Several simulations with synthetic and real datacubes show promising results as the reliable reconstruction of a high spectral and spatial resolution image can be achieved by using as few as just the 50% of the datacube.

  3. Study on airborne multispectral imaging fusion detection technology

    NASA Astrophysics Data System (ADS)

    Ding, Na; Gao, Jiaobo; Wang, Jun; Cheng, Juan; Gao, Meng; Gao, Fei; Fan, Zhe; Sun, Kefeng; Wu, Jun; Li, Junna; Gao, Zedong; Cheng, Gang

    2014-11-01

    The airborne multispectral imaging fusion detection technology is proposed in this paper. In this design scheme, the airborne multispectral imaging system consists of the multispectral camera, the image processing unit, and the stabilized platform. The multispectral camera can operate in the spectral region from visible to near infrared waveband (0.4-1.0um), it has four same and independent imaging channels, and sixteen different typical wavelengths to be selected based on the different typical targets and background. The related experiments were tested by the airborne multispectral imaging system. In particularly, the camouflage targets were fused and detected in the different complex environment, such as the land vegetation background, the desert hot background and underwater. In the spectral region from 0.4 um to 1.0um, the three different characteristic wave from sixteen typical spectral are selected and combined according to different backgrounds and targets. The spectral image corresponding to the three characteristic wavelengths is resisted and fused by the image processing technology in real time, and the fusion video with typical target property is outputted. In these fusion images, the contrast of target and background is greatly increased. Experimental results confirm that the airborne multispectral imaging fusion detection technology can acquire multispectral fusion image with high contrast in real time, and has the ability of detecting and identification camouflage objects from complex background to targets underwater.

  4. Multispectral image analysis of bruise age

    NASA Astrophysics Data System (ADS)

    Sprigle, Stephen; Yi, Dingrong; Caspall, Jayme; Linden, Maureen; Kong, Linghua; Duckworth, Mark

    2007-03-01

    The detection and aging of bruises is important within clinical and forensic environments. Traditionally, visual and photographic assessment of bruise color is used to determine age, but this substantially subjective technique has been shown to be inaccurate and unreliable. The purpose of this study was to develop a technique to spectrally-age bruises using a reflective multi-spectral imaging system that minimizes the filtering and hardware requirements while achieving acceptable accuracy. This approach will then be incorporated into a handheld, point-of-care technology that is clinically-viable and affordable. Sixteen bruises from elder residents of a long term care facility were imaged over time. A multi-spectral system collected images through eleven narrow band (~10 nm FWHM) filters having center wavelengths ranging between 370-970 nm corresponding to specific skin and blood chromophores. Normalized bruise reflectance (NBR)- defined as the ratio of optical reflectance coefficient of bruised skin over that of normal skin- was calculated for all bruises at all wavelengths. The smallest mean NBR, regardless of bruise age, was found at wavelength between 555 & 577nm suggesting that contrast in bruises are from the hemoglobin, and that they linger for a long duration. A contrast metric, based on the NBR at 460nm and 650nm, was found to be sensitive to age and requires further investigation. Overall, the study identified four key wavelengths that have promise to characterize bruise age. However, the high variability across the bruises imaged in this study complicates the development of a handheld detection system until additional data is available.

  5. Multispectral imaging with type II superlattice detectors

    NASA Astrophysics Data System (ADS)

    Ariyawansa, Gamini; Duran, Joshua M.; Grupen, Matt; Scheihing, John E.; Nelson, Thomas R.; Eismann, Michael T.

    2012-06-01

    Infrared (IR) focal plane arrays (FPAs) with multispectral detector elements promise significant advantages for airborne threat warning, surveillance, and targeting applications. At present, the use of type II superlattice (T2SL) structures based on the 6.1Å-family materials (InAs, GaSb, and AlSb) has become an area of interest for developing IR detectors and their FPAs. The ability to vary the bandgap in the IR range, suppression of Auger processes, prospective reduction of Shockley-Read-Hall centers by improved material growth capabilities, and the material stability are a few reasons for the predicted dominance of the T2SL technology over presently leading HgCdTe and quantum well technologies. The focus of the work reported here is on the development of T2SL based dual-band IR detectors and their applicability for multispectral imaging. A new NpBPN detector designed for the detection of IR in the 3-5 and 8-12 μm atmospheric windows is presented; comparing its advantages over other T2SL based approaches. One of the key challenges of the T2SL dual-band detectors is the spectral crosstalk associated with the LWIR band. The properties of the state-of-the-art T2SLs (i.e., absorption coefficient, minority carrier lifetime and mobility, etc.) and the present growth limitations that impact spectral crosstalk are discussed.

  6. Novel multispectral imaging microscope with applications to biomedicine

    NASA Astrophysics Data System (ADS)

    Zeng, Libo; Wu, Qiongshui; Ke, Hengyu; Zheng, Hong; Hu, Yaojun; Ding, Yi

    2005-03-01

    This paper describes a novel multispectral imaging microscope that can simultaneously record both spectral and spatial information of a sample, which can take advantage of spatial image processing and spectroscopic analysis techniques. A Liquid Crystal Tunable Filter device is used for fast wavelength selection and a cooled two-dimensional monochrome CCD for image detection. In order to acquire images that are not so dependent on imaging devices, a clever CCD exposure time control and a software based spectral and spatial calibration process is performed to diminish the influence of illumination, optic ununiformity, CCD"s spectral response curve and optic throughput property. A set of multispectral image processing and analysis software package is developed, which covers not only general image processing and analysis functions, and also provides powerful analysis tools for multispectral image data, including multispectral image acquisition, illumination and system response calibration, spectral analysis and etc. The combination of spatial and spectral analysis makes it an ideal tool for the applications to biomedicine. In this paper, two applications in biomedicine are also presented. One is medical image segmentation. Using multispectral imaging techniques, a mass of experiments on both marrow bone and cervical cell images showed that our segmentation results are highly satisfactory while with low computational cost. Another is biological imaging spectroscopic analysis in the study of pollen grains in rice. The results showed that the transmittance analysis of multispectral pollen images can accurately identify the pollen abortion stage of male-sterile rice, and can easily distinguish a variety of male sterile cytoplasm.

  7. A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

    PubMed

    Jiang, Dong; Huang, Yaohuan; Zhuang, Dafang; Zhu, Yunqiang; Xu, Xinliang; Ren, Hongyan

    2012-01-01

    Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.

  8. A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

    PubMed Central

    Jiang, Dong; Huang, Yaohuan; Zhuang, Dafang; Zhu, Yunqiang; Xu, Xinliang; Ren, Hongyan

    2012-01-01

    Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience. PMID:23049886

  9. Multispectral Imaging for Determination of Astaxanthin Concentration in Salmonids

    PubMed Central

    Dissing, Bjørn S.; Nielsen, Michael E.; Ersbøll, Bjarne K.; Frosch, Stina

    2011-01-01

    Multispectral imaging has been evaluated for characterization of the concentration of a specific cartenoid pigment; astaxanthin. 59 fillets of rainbow trout, Oncorhynchus mykiss, were filleted and imaged using a rapid multispectral imaging device for quantitative analysis. The multispectral imaging device captures reflection properties in 19 distinct wavelength bands, prior to determination of the true concentration of astaxanthin. The samples ranged from 0.20 to 4.34 g per g fish. A PLSR model was calibrated to predict astaxanthin concentration from novel images, and showed good results with a RMSEP of 0.27. For comparison a similar model were built for normal color images, which yielded a RMSEP of 0.45. The acquisition speed of the multispectral imaging system and the accuracy of the PLSR model obtained suggest this method as a promising technique for rapid in-line estimation of astaxanthin concentration in rainbow trout fillets. PMID:21573000

  10. Comparative performance analysis of stained histopathology specimens using RGB and multispectral imaging

    NASA Astrophysics Data System (ADS)

    Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

    2011-03-01

    A performance study was conducted to compare classification accuracy using both multispectral imaging (MSI) and standard bright-field imaging (RGB) to characterize breast tissue microarrays. The study was primarily focused on investigating the classification power of texton features for differentiating cancerous breast TMA discs from normal. The feature extraction algorithm includes two main processes: texton library training and histogram construction. First, two texton libraries were built for multispectral cubes and RGB images respectively, which comprised the training process. Second, texton histograms from each multispectral cube and RGB image were used as testing sets. Finally, within each spectral band, exhaustive feature selection was used to search for the combination of features that yielded the best classification accuracy using the pathologic result as a golden standard. Support vector machine was applied as a classifier using leave-one-out cross-validation. The spectra carrying the greatest discriminatory power were automatically chosen and a majority vote was used to make the final classification. The study included 122 breast TMA discs that showed poor classification power based on simple visualization of RGB images. Use of multispectral cubes showed improved sensitivity and specificity compared to the RGB images (85% sensitivity & 85% specificity for MSI vs. 75% & 65% for RGB). This study demonstrates that use of texton features derived from MSI datasets achieve better classification accuracy than those derived from RGB datasets. This study further shows that MSI provided statistically significant improvements in automated analysis of single-stained bright-field images. Future work will examine MSI performance in assessing multistained specimens.

  11. In vivo simultaneous multispectral fluorescence imaging with spectral multiplexed volume holographic imaging system

    NASA Astrophysics Data System (ADS)

    Lv, Yanlu; Zhang, Jiulou; Zhang, Dong; Cai, Wenjuan; Chen, Nanguang; Luo, Jianwen

    2016-06-01

    A simultaneous multispectral fluorescence imaging system incorporating multiplexed volume holographic grating (VHG) is developed to acquire multispectral images of an object in one shot. With the multiplexed VHG, the imaging system can provide the distribution and spectral characteristics of multiple fluorophores in the scene. The implementation and performance of the simultaneous multispectral imaging system are presented. Further, the system's capability in simultaneously obtaining multispectral fluorescence measurements is demonstrated with in vivo experiments on a mouse. The demonstrated imaging system has the potential to obtain multispectral images fluorescence simultaneously.

  12. Eliminate background interference from latent fingerprints using ultraviolet multispectral imaging

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Xu, Xiaojing; Wang, Guiqiang

    2014-02-01

    Fingerprints are the most important evidence in crime scene. The technology of developing latent fingerprints is one of the hottest research areas in forensic science. Recently, multispectral imaging which has shown great capability in fingerprints development, questioned document detection and trace evidence examination is used in detecting material evidence. This paper studied how to eliminate background interference from non-porous and porous surface latent fingerprints by rotating filter wheel ultraviolet multispectral imaging. The results approved that background interference could be removed clearly from latent fingerprints by using multispectral imaging in ultraviolet bandwidth.

  13. Fast Lossless Compression of Multispectral-Image Data

    NASA Technical Reports Server (NTRS)

    Klimesh, Matthew

    2006-01-01

    An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive-filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general.

  14. On-board multispectral classification study. Volume 2: Supplementary tasks. [adaptive control

    NASA Technical Reports Server (NTRS)

    Ewalt, D.

    1979-01-01

    The operational tasks of the onboard multispectral classification study were defined. These tasks include: sensing characteristics for future space applications; information adaptive systems architectural approaches; data set selection criteria; and onboard functional requirements for interfacing with global positioning satellites.

  15. Image quality (IQ) guided multispectral image compression

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Chen, Genshe; Wang, Zhonghai; Blasch, Erik

    2016-05-01

    Image compression is necessary for data transportation, which saves both transferring time and storage space. In this paper, we focus on our discussion on lossy compression. There are many standard image formats and corresponding compression algorithms, for examples, JPEG (DCT -- discrete cosine transform), JPEG 2000 (DWT -- discrete wavelet transform), BPG (better portable graphics) and TIFF (LZW -- Lempel-Ziv-Welch). The image quality (IQ) of decompressed image will be measured by numerical metrics such as root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural Similarity (SSIM) Index. Given an image and a specified IQ, we will investigate how to select a compression method and its parameters to achieve an expected compression. Our scenario consists of 3 steps. The first step is to compress a set of interested images by varying parameters and compute their IQs for each compression method. The second step is to create several regression models per compression method after analyzing the IQ-measurement versus compression-parameter from a number of compressed images. The third step is to compress the given image with the specified IQ using the selected compression method (JPEG, JPEG2000, BPG, or TIFF) according to the regressed models. The IQ may be specified by a compression ratio (e.g., 100), then we will select the compression method of the highest IQ (SSIM, or PSNR). Or the IQ may be specified by a IQ metric (e.g., SSIM = 0.8, or PSNR = 50), then we will select the compression method of the highest compression ratio. Our experiments tested on thermal (long-wave infrared) images (in gray scales) showed very promising results.

  16. Compact multi-spectral imaging system for dermatology and neurosurgery

    NASA Astrophysics Data System (ADS)

    Noordmans, Herke Jan; de Roode, Rowland; Verdaasdonk, Rudolf

    2007-03-01

    A compact multi-spectral imaging system is presented as diagnostic tool in dermatology and neurosurgery. Using an electronically tunable filter, a sensitive high resolution digital camera, 140 spectral images from 400 nm up to 720 nm are acquired in 40 s. Advanced image processing algorithms are used to enable interactive acquisition, viewing, image registration and image analysis. Experiments in the department of dermatology and neurosurgery show that multispectral imaging reveals much more detail than conventional medical photography or a surgical microscope, as images can be reprocessed to enhance the view on e.g. tumor boundaries. Using a hardware-based interactive registration algorithm, multi-spectral images can be aligned to correct for motion occurred during image acquisition or to compare acquisitions from different moments in time. The system shows to be a powerful diagnostics tool for medical imaging in the visual and near IR range.

  17. Pre-Processor for Compression of Multispectral Image Data

    NASA Technical Reports Server (NTRS)

    Klimesh, Matthew; Kiely, Aaron

    2006-01-01

    A computer program that preprocesses multispectral image data has been developed to provide the Mars Exploration Rover (MER) mission with a means of exploiting the additional correlation present in such data without appreciably increasing the complexity of compressing the data.

  18. Multispectral Stokes polarimetry for dermatoscopic imaging

    NASA Astrophysics Data System (ADS)

    Castillejos, Y.; Martínez-Ponce, Geminiano; Mora-Nuñez, Azael; Castro-Sanchez, R.

    2015-12-01

    Most of skin pathologies, including melanoma and basal/squamous cell carcinoma, are related to alterations in external and internal order. Usually, physicians rely on their empirical expertise to diagnose these ills normally assisted with dermatoscopes. When there exists skin cancer suspicion, a cytology or biopsy is made, but both laboratory tests imply an invasive procedure. In this regard, a number of non-invasive optical techniques have been proposed recently to improve the diagnostic certainty and assist in the early detection of cutaneous cancer. Herein, skin optical properties are derived with a multispectral polarimetric dermatoscope using three different illumination wavelength intervals centered at 470, 530 and 635nm. The optical device consist of two polarizing elements, a quarter-wave plate and a linear polarizer, rotating at a different angular velocity and a CCD array as the photoreceiver. The modulated signal provided by a single pixel in the acquired image sequence is analyzed with the aim of computing the Stokes parameters. Changes in polarization state of selected wavelengths provide information about the presence of skin pigments such as melanin and hemoglobin species as well as collagen structure, among other components. These skin attributes determine the local physiology or pathology. From the results, it is concluded that optical polarimetry will provide additional elements to dermatologists in their diagnostic task.

  19. Integration of multispectral and C-band SAR data for crop classification

    NASA Astrophysics Data System (ADS)

    Ianninia, L.; Molijn, R. A.; Hanssen, R. F.

    2013-10-01

    The paper debates the impact of sensor configuration diversity on the crop classification performance. More specifically, the analysis accounts for multi-temporal and polarimetric C-Band SAR information used individually and in synergy with Multispectral imagery. The dataset used for the investigation comprises several multi-angle Radarsat-2 (RS2) fullpol acquisitions and RapidEye (RE) images both at fine resolution collected over the Indian Head (Canada) agricultural site area and spanning the summer crop growth cycle from May to September. A quasi-Maximum Likelihood (ML) classification approach applied at per-field level has been adopted to integrate the different data sources. The analysis provided evidence on the overall accuracy enhancement with respect to the individual sensor performances, with 4%-8% increase over a single RE image, a 40%-10% increase over a single 1-pol/full-pol image and 15%-0% increase over multitemporal 1-pol/full-pol RS2 series respectively. A more detailed crop analysis revealed that in particular canola and the cereals benefit from the integration, whereas lentil and flax can experience similar or worse performance when compared to the RE-based classification. Comments and suggestions for further development are presented.

  20. Multispectral Microscopic Imager (MMI): Multispectral Imaging of Geological Materials at a Handlens Scale

    NASA Astrophysics Data System (ADS)

    Farmer, J. D.; Nunez, J. I.; Sellar, R. G.; Gardner, P. B.; Manatt, K. S.; Dingizian, A.; Dudik, M. J.; McDonnell, G.; Le, T.; Thomas, J. A.; Chu, K.

    2011-12-01

    The Multispectral Microscopic Imager (MMI) is a prototype instrument presently under development for future astrobiological missions to Mars. The MMI is designed to be a arm-mounted rover instrument for use in characterizing the microtexture and mineralogy of materials along geological traverses [1,2,3]. Such geological information is regarded as essential for interpreting petrogenesis and geological history, and when acquired in near real-time, can support hypothesis-driven exploration and optimize science return. Correlated microtexure and mineralogy also provides essential data for selecting samples for analysis with onboard lab instruments, and for prioritizing samples for potential Earth return. The MMI design employs multispectral light-emitting diodes (LEDs) and an uncooled focal plane array to achieve the low-mass (<1kg), low-cost, and high reliability (no moving parts) required for an arm-mounted instrument on a planetary rover [2,3]. The MMI acquires multispectral, reflectance images at 62 μm/pixel, in which each image pixel is comprised of a 21-band VNIR spectrum (0.46 to 1.73 μm). This capability enables the MMI to discriminate and resolve the spatial distribution of minerals and textures at the microscale [2, 3]. By extending the spectral range into the infrared, and increasing the number of spectral bands, the MMI exceeds the capabilities of current microimagers, including the MER Microscopic Imager (MI); 4, the Phoenix mission Robotic Arm Camera (RAC; 5) and the Mars Science Laboratory's Mars Hand Lens Imager (MAHLI; 6). In this report we will review the capabilities of the MMI by highlighting recent lab and field applications, including: 1) glove box deployments in the Astromaterials lab at Johnson Space Center to analyze Apollo lunar samples; 2) GeoLab glove box deployments during the 2011 Desert RATS field trials in northern AZ to characterize analog materials collected by astronauts during simulated EVAs; 3) field deployments on Mauna Kea

  1. Lossless compression of multispectral images using spectral information

    NASA Astrophysics Data System (ADS)

    Ma, Long; Shi, Zelin; Tang, Xusheng

    2009-10-01

    Multispectral images are available for different purposes due to developments in spectral imaging systems. The sizes of multispectral images are enormous. Thus transmission and storage of these volumes of data require huge time and memory resources. That is why compression algorithms must be developed. A salient property of multispectral images is that strong spectral correlation exists throughout almost all bands. This fact is successfully used to predict each band based on the previous bands. We propose to use spectral linear prediction and entropy coding with context modeling for encoding multispectral images. Linear prediction predicts the value for the next sample and computes the difference between predicted value and the original value. This difference is usually small, so it can be encoded with less its than the original value. The technique implies prediction of each image band by involving number of bands along the image spectra. Each pixel is predicted using information provided by pixels in the previous bands in the same spatial position. As done in the JPEG-LS, the proposed coder also represents the mapped residuals by using an adaptive Golomb-Rice code with context modeling. This residual coding is context adaptive, where the context used for the current sample is identified by a context quantization function of the three gradients. Then, context-dependent Golomb-Rice code and bias parameters are estimated sample by sample. The proposed scheme was compared with three algorithms applied to the lossless compression of multispectral images, namely JPEG-LS, Rice coding, and JPEG2000. Simulation tests performed on AVIRIS images have demonstrated that the proposed compression scheme is suitable for multispectral images.

  2. Multispectral Digital Image Analysis of Varved Sediments in Thin Sections

    NASA Astrophysics Data System (ADS)

    Jäger, K.; Rein, B.; Dietrich, S.

    2006-12-01

    An update of the recently developed method COMPONENTS (Rein, 2003, Rein & Jäger, subm.) for the discrimination of sediment components in thin sections is presented here. COMPONENTS uses a 6-band (multispectral) image analysis. To derive six-band spectral information of the sediments, thin sections are scanned with a digital camera mounted on a polarizing microscope. The thin sections are scanned twice, under polarized and under unpolarized plain light. During each run RGB images are acquired which are subsequently stacked to a six-band file. The first three bands (Blue=1, Green=2, Red=3) result from the spectral behaviour in the blue, green and red band with unpolarized light conditions, and the bands 4 to 6 (Blue=4, Green=5, Red=6) from the polarized light run. The next step is the discrimination of the sediment components by their transmission behaviour. Automatic classification algorithms broadly used in remote sensing applications cannot be used due to unavoidable variations of sediment particle or thin section thicknesses that change absolute grey values of the sediment components. Thus, we use an approach based on band ratios, also known as indices. By using band ratios, the grey values measured in different bands are normalized against each other and illumination variations (e.g. thickness variations) are eliminated. By combining specific ratios we are able to detect all seven major components in the investigated sediments (carbonates, diatoms, fine clastic material, plant rests, pyrite, quartz and resin). Then, the classification results (compositional maps) are validated. Although the automatic classification and the analogous classification show high concordances, some systematic errors could be identified. For example, the transition zone between the sediment and resin filled cracks is classified as fine clastic material and very coarse carbonates are partly classified as quartz because coarse carbonates can be very bright and spectra are partly

  3. Online Variety Discrimination of Rice Seeds Using Multispectral Imaging and Chemometric Methods

    NASA Astrophysics Data System (ADS)

    Liu, W.; Liu, Ch.; Ma, F.; Lu, X.; Yang, J.; Zheng, L.

    2016-01-01

    Variety identification plays an important role in ensuring the quality and quantity of yield in rice production. The feasibility of a rapid and nondestructive determination of varieties of rice seeds was examined by using a multispectral imaging system combined with chemometric data analysis. Methods of the partial least squares discriminant analysis (PLSDA), principal component analysis-back propagation neural network (PCA-BPNN), and least squares-support vector machines (LS-SVM) were applied to classify varieties of rice seeds. The results demonstrate that clear differences among varieties of rice seeds could be easily visualized using the multispectral imaging technique and an excellent classification could be achieved combining data of the spectral and morphological features. The classification accuracy was up to 94% in a validation set with the LS-SVM model, which was better than the PLSDA (62%) and PCA-BPNN (84%) models.

  4. Systemically diseased chicken identification using multispectral images and region of interest analysis

    NASA Astrophysics Data System (ADS)

    Yang, Chun-Chieh; Chao, Kuanglin; Chen, Yud-Ren; Early, Howard L.

    2004-11-01

    A simple multispectral classification method for the identification of systemically diseased chickens was developed and tested between two different imaging systems. An image processing algorithm was developed to define and locate the region of interest (ROI) as classification areas on the image. The average intensity was calculated for each classification area of the chicken image. A decision tree algorithm was used to determine threshold values for each classification areas. The wavelength of 540 nm was used for image differentiation purpose. There were 164 wholesome and 176 systemically diseased chicken images collected using the first imaging system, and 332 wholesome and 318 systemically diseased chicken images taken by the second imaging system. The differentiation thresholds, generated by the decision tree method, based on the images from the first imaging system were applied to the images from the second imaging system, and vice versa. The accuracy from evaluation was 95.7% for wholesome and 97.7% of systemically diseased chickens for the first image batch, and 99.7% for wholesome and 93.5% for systemically diseased chickens for the second image batch. The result showed that using single wavelength and threshold, this simple classification method can be used in automated on-line applications for chicken inspection.

  5. Nondestructive prediction of pork freshness parameters using multispectral scattering images

    NASA Astrophysics Data System (ADS)

    Tang, Xiuying; Li, Cuiling; Peng, Yankun; Chao, Kuanglin; Wang, Mingwu

    2012-05-01

    Optical technology is an important and immerging technology for non-destructive and rapid detection of pork freshness. This paper studied on the possibility of using multispectral imaging technique and scattering characteristics to predict the freshness parameters of pork meat. The pork freshness parameters selected for prediction included total volatile basic nitrogen (TVB-N), color parameters (L *, a *, b *), and pH value. Multispectral scattering images were obtained from pork sample surface by a multispectral imaging system developed by ourselves; they were acquired at the selected narrow wavebands whose center wavelengths were 517,550, 560, 580, 600, 760, 810 and 910nm. In order to extract scattering characteristics from multispectral images at multiple wavelengths, a Lorentzian distribution (LD) function with four parameters (a: scattering asymptotic value; b: scattering peak; c: scattering width; d: scattering slope) was used to fit the scattering curves at the selected wavelengths. The results show that the multispectral imaging technique combined with scattering characteristics is promising for predicting the freshness parameters of pork meat.

  6. Multispectral image visualization through first-order fusion.

    PubMed

    Socolinsky, Diego A; Wolff, Lawrence B

    2002-01-01

    We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first-order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first-order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed. A variety of experimental results are presented to support the performance of the new method. PMID:18244686

  7. Automatic recognition of abnormal cells in cytological tests using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Gertych, A.; Galliano, G.; Bose, S.; Farkas, D. L.

    2010-03-01

    Cervical cancer is the leading cause of gynecologic disease-related death worldwide, but is almost completely preventable with regular screening, for which cytological testing is a method of choice. Although such testing has radically lowered the death rate from cervical cancer, it is plagued by low sensitivity and inter-observer variability. Moreover, its effectiveness is still restricted because the recognition of shape and morphology of nuclei is compromised by overlapping and clumped cells. Multispectral imaging can aid enhanced morphological characterization of cytological specimens. Features including spectral intensity and texture, reflecting relevant morphological differences between normal and abnormal cells, can be derived from cytopathology images and utilized in a detection/classification scheme. Our automated processing of multispectral image cubes yields nuclear objects which are subjected to classification facilitated by a library of spectral signatures obtained from normal and abnormal cells, as marked by experts. Clumps are processed separately with reduced set of signatures. Implementation of this method yields high rate of successful detection and classification of nuclei into predefined malignant and premalignant types and correlates well with those obtained by an expert. Our multispectral approach may have an impact on the diagnostic workflow of cytological tests. Abnormal cells can be automatically highlighted and quantified, thus objectivity and performance of the reading can be improved in a way which is currently unavailable in clinical setting.

  8. Utilization of LANDSAT-TM and SPOT multispectral raw and integrated data for land cover classification

    NASA Astrophysics Data System (ADS)

    Salajanu, Dumitru

    The work of this dissertation presents results obtained from using LANDSAT-TM and SPOT multispectral raw and integrated image data for land cover/use classification with an emphasis on forest type discrimination. The main objective of this study was to find out to what degree information from satellites with different spectral and spatial resolution can be integrated and used to improve the overall and individual (cover type) classification accuracy particularly in forestry. Three hypotheses were formulated in order to test the main objective. The test site located in northwestern Washtenaw County, Michigan includes areas within and outside of Stinchfield Woods and consists of a large diversity of species especially conifer plantations. The LANDSAT-TM and SPOT-XS raw data were registered to the SPOT Panchromatic data. Conjugate ground control points collected from both images were used to produce image to image registration. Once the images were registered to each other several algorithms were used to merge the two images into a new one. Raw and integrated image data were subjected to radiometric and spectral enhancements (contrast stretching, NDVI ratio) and finally used in supervised and unsupervised classifications. Several supervised classification trials were completed for each hypothesis tested using raw and integrated data and the Maximum Likelihood algorithm. The masking process was used to segment the test area into more homogeneous cover types, which resulted in improved overall classification accuracy. Reference maps were prepared for both (TM and SPOT-XS) raw and integrated classified images from two enlarged aerial photographs. Classified maps from both raw and integrated data were tested and evaluated by interpreting contingency tables using several statistics (PCC, Cohen's Kappa) to characterize overall classification accuracy. Based on the test results the following conclusions were drawn. Overall classification accuracy from satellite data

  9. Improving performance of real-time multispectral imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A real-time multispectral imaging system can be a science-based tool for fecal and ingesta contaminant detection during poultry processing. For the implementation of this imaging system at commercial poultry processing plant, false positive errors must be removed. For doing this, we tested and imp...

  10. Filter selection based on light source for multispectral imaging

    NASA Astrophysics Data System (ADS)

    Xu, Peng; Xu, Haisong

    2016-07-01

    In multispectral imaging, it is necessary to select a reduced number of filters to balance the imaging efficiency and spectral reflectance recovery accuracy. Due to the combined effect of filters and light source on reflectance recovery, the optimal filters are influenced by the employed light source in the multispectral imaging system. By casting the filter selection as an optimization issue, the selection of optimal filters corresponding to the employed light source proceeds with respect to a set of target samples utilizing one kind of genetic algorithms, regardless of the detailed spectral characteristics of the light source, filters, and sensor. Under three light sources with distinct spectral power distributions, the proposed filter selection method was evaluated on a filter-wheel based multispectral device with a set of interference filters. It was verified that the filters derived by the proposed method achieve better spectral and colorimetric accuracy of reflectance recovery than the conventional one under different light sources.

  11. The Multispectral Imaging Science Working Group. Volume 2: Working group reports

    NASA Technical Reports Server (NTRS)

    Cox, S. C. (Editor)

    1982-01-01

    Summaries of the various multispectral imaging science working groups are presented. Current knowledge of the spectral and spatial characteristics of the Earth's surface is outlined and the present and future capabilities of multispectral imaging systems are discussed.

  12. Semiconductor Laser Multi-Spectral Sensing and Imaging

    PubMed Central

    Le, Han Q.; Wang, Yang

    2010-01-01

    Multi-spectral laser imaging is a technique that can offer a combination of the laser capability of accurate spectral sensing with the desirable features of passive multispectral imaging. The technique can be used for detection, discrimination, and identification of objects by their spectral signature. This article describes and reviews the development and evaluation of semiconductor multi-spectral laser imaging systems. Although the method is certainly not specific to any laser technology, the use of semiconductor lasers is significant with respect to practicality and affordability. More relevantly, semiconductor lasers have their own characteristics; they offer excellent wavelength diversity but usually with modest power. Thus, system design and engineering issues are analyzed for approaches and trade-offs that can make the best use of semiconductor laser capabilities in multispectral imaging. A few systems were developed and the technique was tested and evaluated on a variety of natural and man-made objects. It was shown capable of high spectral resolution imaging which, unlike non-imaging point sensing, allows detecting and discriminating objects of interest even without a priori spectroscopic knowledge of the targets. Examples include material and chemical discrimination. It was also shown capable of dealing with the complexity of interpreting diffuse scattered spectral images and produced results that could otherwise be ambiguous with conventional imaging. Examples with glucose and spectral imaging of drug pills were discussed. Lastly, the technique was shown with conventional laser spectroscopy such as wavelength modulation spectroscopy to image a gas (CO). These results suggest the versatility and power of multi-spectral laser imaging, which can be practical with the use of semiconductor lasers. PMID:22315555

  13. Evaluating the Potential of Multispectral Airborne LIDAR for Topographic Mapping and Land Cover Classification

    NASA Astrophysics Data System (ADS)

    Wichmann, V.; Bremer, M.; Lindenberger, J.; Rutzinger, M.; Georges, C.; Petrini-Monteferri, F.

    2015-08-01

    Recently multispectral LiDAR became a promising research field for enhanced LiDAR classification workflows and e.g. the assessment of vegetation health. Current analyses on multispectral LiDAR are mainly based on experimental setups, which are often limited transferable to operational tasks. In late 2014 Optech Inc. announced the first commercially available multispectral LiDAR system for airborne topographic mapping. The combined system makes synchronic multispectral LiDAR measurements possible, solving time shift problems of experimental acquisitions. This paper presents an explorative analysis of the first airborne collected data with focus on class specific spectral signatures. Spectral patterns are used for a classification approach, which is evaluated in comparison to a manual reference classification. Typical spectral patterns comparable to optical imagery could be observed for homogeneous and planar surfaces. For rough and volumetric objects such as trees, the spectral signature becomes biased by signal modification due to multi return effects. However, we show that this first flight data set is suitable for conventional geometrical classification and mapping procedures. Additional classes such as sealed and unsealed ground can be separated with high classification accuracies. For vegetation classification the distinction of species and health classes is possible.

  14. Spatial arrangement of color filter array for multispectral image acquisition

    NASA Astrophysics Data System (ADS)

    Shrestha, Raju; Hardeberg, Jon Y.; Khan, Rahat

    2011-03-01

    In the past few years there has been a significant volume of research work carried out in the field of multispectral image acquisition. The focus of most of these has been to facilitate a type of multispectral image acquisition systems that usually requires multiple subsequent shots (e.g. systems based on filter wheels, liquid crystal tunable filters, or active lighting). Recently, an alternative approach for one-shot multispectral image acquisition has been proposed; based on an extension of the color filter array (CFA) standard to produce more than three channels. We can thus introduce the concept of multispectral color filter array (MCFA). But this field has not been much explored, particularly little focus has been given in developing systems which focuses on the reconstruction of scene spectral reflectance. In this paper, we have explored how the spatial arrangement of multispectral color filter array affects the acquisition accuracy with the construction of MCFAs of different sizes. We have simulated acquisitions of several spectral scenes using different number of filters/channels, and compared the results with those obtained by the conventional regular MCFA arrangement, evaluating the precision of the reconstructed scene spectral reflectance in terms of spectral RMS error, and colorimetric ▵E*ab color differences. It has been found that the precision and the the quality of the reconstructed images are significantly influenced by the spatial arrangement of the MCFA and the effect will be more and more prominent with the increase in the number of channels. We believe that MCFA-based systems can be a viable alternative for affordable acquisition of multispectral color images, in particular for applications where spatial resolution can be traded off for spectral resolution. We have shown that the spatial arrangement of the array is an important design issue.

  15. Multispectral data acquisition and classification - Computer modeling for smart sensor design

    NASA Technical Reports Server (NTRS)

    Park, S. K.; Davis, R. E.; Huck, F. O.; Arduini, R. F.

    1980-01-01

    In this paper a model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing and evaluating smart sensors. The model has both stochastic and deterministic elements and accounts for solar radiation, atmospheric radiative transfer, surface reflectance, sensor spectral reponses, and classification algorithms. Preliminary results are presented which indicate the validity and usefulness of this approach. Future capabilities of smart sensors will ultimately be limited by the accuracy with which multispectral remote sensing processes and their error sources can be computationally modeled.

  16. Investigation related to multispectral imaging systems

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F.; Erickson, J. D.

    1974-01-01

    A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

  17. A review and analysis of neural networks for classification of remotely sensed multispectral imagery

    NASA Technical Reports Server (NTRS)

    Paola, Justin D.; Schowengerdt, Robert A.

    1993-01-01

    A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

  18. Characteristic variogram for land use in Multispectral Images

    NASA Astrophysics Data System (ADS)

    Mera, E.; Condal, A.; Rios, C.; Da Silva, L.

    2016-05-01

    In remote sensing is the concept of spectral signature in multispectral imagery to recognize different land uses in the area; This study proposes the existence of a characteristic variogram for land use in multispectral images. To test this idea we proceeded to work with a sector of a scene image of multispectral Landsat 7 ETM +, in 6 of their bands (1- 450nm to 520nm, 2 - 520nm to 600nm, 3 - 630nm to 690nm, 4 - 760nm to 900nm 5 - over 1550nm to 1.750nm and 7 - 2.080nm to 2.350nm), corresponding to two uses of urban land and agricultural, the omnidirectional variogram for each band was analyzed and modal variogram for each land use was established in the stripe set. Of the analyzed claims data for each land use is a model characteristic and modal cross variogram how their wavelengths.

  19. Demosaicking for multispectral images based on vectorial total variation

    NASA Astrophysics Data System (ADS)

    Shinoda, Kazuma; Hamasaki, Taisuke; Kawase, Maru; Hasegawa, Madoka; Kato, Shigeo

    2016-08-01

    Multispectral images (MSIs), which consist of more color components than RGB images, can be used in the field of vegetation analysis and medical imaging. A capturing system with multispectral filter array (MSFA) technology has been researched to shorten the capturing time and reduce the cost. In this system, the mosaicked image captured by the MSFA is demosaicked to reconstruct the MSI. We propose a demosaicking method using vectorial total variation (VTV) regularization for an MSI. This process is regarded as inverse problem of the image observation model. The reconstructed image is estimated by minimizing the VTV as a regularization term under the constraint condition. In the experimental results, the reconstructed image quality obtained using the proposed method is better than that of the conventional approaches in terms of both peak signal-to-noise ratio and structural similarity.

  20. Trophic classification of Colorado lakes utilizing contact data, Landsat and aircraft-acquired multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Boland, D. H. P.; Blackwell, R. J.

    1978-01-01

    Multispectral scanner data, acquired over several Colorado lakes using Landsat-1 and aircraft, were used in conjunction with National Eutrophication Survey contact-sensed data to determine the feasibility of assessing lacustrine trophic levels. A trophic state index was developed using contact-sensed data for several trophic indicators (chlorophyll a, inverse of Secchi disk transparency, conductivity, total phosphorous, total organic nitrogen, algal assay yield). Relationships between the digitally processed multispectral scanner data, several trophic indicators, and the trophic index were examined using a supervised multispectral classification technique and regression techniques. Statistically significant correlations exist between spectral bands, several of the trophic indicators (chlorophyll a, Secchi disk transparency, total organic nitrogen), and the trophic state index. Color-coded photomaps were generated which depict the spectral aspects of trophic state. Multispectral scanner data acquired from satellite and aircraft platforms can be used to advantage in lake monitoring and survey programs.

  1. Improving classification of hydrogeomorphic features in a gravel-bed river using an object-oriented fuzzy classification of multispectral satellite and LiDAR terrain data

    NASA Astrophysics Data System (ADS)

    Aggett, G. R.

    2012-12-01

    Recent attempts to map hydrogeomorphic objects by automatically classifying high spatial and spectral resolution data have tended to yield somewhat unsatisfactory results. This paper suggests that the main reason for this is the inherent limitations of image processing techniques that use a per-pixel approach to spectral classification, and their tendency to ignore spatial characteristics and relationships of hydrogeomorphic objects in the classification process. Pixel-based classifications have problems adequately or conveniently exploiting contextual information or expert knowledge. Object-based image-processing techniques may overcome these difficulties by first segmenting the image into meaningful multi-pixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels. Objects are assigned classes using fuzzy logic and a hierarchical decision key. This is tested here in the fluvial domain by comparing a per-pixel classification of a gravel-bed river to an object-oriented fuzzy classifier, using a readily available and relatively inexpensive high resolution satellite dataset that can be ordered for a specific date either in the future, or from an image library. Despite improved results using the object-oriented method, we also assert and demonstrate that the fusion of image data with detailed terrain modeled information is required if we are to make strides in reducing classification ambiguities in complex river systems. Thus a second experiment investigates the utility of fusing a LiDAR dataset with multispectral imagery to enhance the object-oriented image classification.

  2. Toward automatic mitotic cell detection and segmentation in multispectral histopathological images.

    PubMed

    Lu, Cheng; Mandal, Mrinal

    2014-03-01

    The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 × 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81.5% sensitivity rate and 33.9% precision rate in terms of detection performance, and 89.3% sensitivity rate and 87.5% precision rate in terms of segmentation performance.

  3. Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

    NASA Astrophysics Data System (ADS)

    Papadomanolaki, M.; Vakalopoulou, M.; Zagoruyko, S.; Karantzalos, K.

    2016-06-01

    In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

  4. Terahertz and multispectral imaging of a Tanda painting

    NASA Astrophysics Data System (ADS)

    Jackson, J. Bianca; Melis, Marcello; Walker, Gillian; Giovannacci, David; Miccoli, Matteo; Martos-Levif, Dominique; Bowen, John; Detalle, Vincent

    2015-06-01

    We systematically examined the mid-20th century Italian painting "After Fishing" (fig. 1) by Ausonio Tanda using multi-spectral (UV, RGB visible, tri-band IR), x-ray and terahertz time-domain spectroscopic imaging. THz-TDSI was performed in both transmission and reflection geometries and the results were compared.

  5. Combining multispectral images and selected textural features from high-resolution images to improve discrimination of forest canopies

    NASA Astrophysics Data System (ADS)

    Ruiz, Luis A.; Inan, Igor; Baridon, Juan E.; Lanfranco, Jorge W.

    1998-12-01

    Discrimination of vegetation canopies for production of forestry and land use thematic cartography from multispectral satellite images requires high spectral and spatial resolutions, usually not available in this type of images. A methodology is proposed to improve a vegetation oriented classification from a Landsat TM image by adding texture information obtained from panchromatic aerial photographs. Multispectral classification was used to create a mask of the forested areas that was applied over the aerial mosaic composition. Further vegetation classes were defined based on textural differences, and eight texture features derived from the gray level co-occurrence matrix, three textural energy indicators and a factor of edgeness were tested. A selection of optimal features and textural parameters such as number of gray levels, window size and distance between pixels was performed using principal components and stepwise discriminant analysis techniques with a set of representative samples from each class. After a texture segmentation of panchromatic aerial imagery using optimal parameters and features was completed, a post-classification process based on morphological operations was applied to avoid the neighboring effect generated by the texture analysis. Overall accuracy in the identification of texture classes using the four best feathers was 86.6%, while the 88% of accuracy was achieved in the classification of the complete image. This method is useful for discrimination of certain vegetation classes with low spectral separability and arranged in small forest units, increasing the classification detail in those areas of particular interest.

  6. Multispectral-image fusion using neural networks

    NASA Astrophysics Data System (ADS)

    Kagel, Joseph H.; Platt, C. A.; Donaven, T. W.; Samstad, Eric A.

    1990-08-01

    A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard a circuit card assembly and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations results and a description of the prototype system are presented. 1.

  7. Multispectral image fusion using neural networks

    NASA Technical Reports Server (NTRS)

    Kagel, J. H.; Platt, C. A.; Donaven, T. W.; Samstad, E. A.

    1990-01-01

    A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard, a circuit card assembly, and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations, results, and a description of the prototype system are presented.

  8. Performance evaluation of supervised change detection tool on DubaiSat-2 multispectral and pansharp images

    NASA Astrophysics Data System (ADS)

    Almatroushi, Hessa R.

    2014-10-01

    Supervised Change Detection Tool (SCDT) is an in-house developed tool in Emirates Institution for Advanced Science and Technology (EIAST). The developed tool is based on Algebra Change Detection algorithm and multi-class Support Vector Machine classifier and is capable of highlighting the areas of change, describing them, and discarding any falsedetections that result from shadow. Further, it can collect the analysis results, which include the change of class an area went through and the overall change percentage of each class defined, in a Microsoft Word document automatically. This paper evaluates the performance of the SCDT, which was initially developed for DubaiSat-1 multispectral images, on DubaiSat-2 multispectral and pansharp images. Moreover, it compares its performance opposed to Change Detection Analysis (i.e. Post-Classification) in ENVI.

  9. Hyperspectral-multispectral line-scan imaging system for automated poultry carcass inspection applications for food safety.

    PubMed

    Chao, K; Yang, C C; Chen, Y R; Kim, M S; Chan, D E

    2007-11-01

    A hyperspectral-multispectral line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. In-plant testing was conducted for chickens on a commercial evisceration line moving at a speed of 70 birds per minute. Hyperspectral image data was acquired for a calibration data set of 543 wholesome and 64 systemically diseased birds and for a testing data set of 381 wholesome and 100 systemically diseased birds. The calibration data set was used to develop the parameters of the imaging system for conducting multispectral inspection based on fuzzy logic detection algorithms using selected key wavelengths. Using a threshold of 0.4 for fuzzy output decision values, multispectral classification was able to achieve 90.6% accuracy for wholesome birds and 93.8% accuracy for systemically diseased birds in the calibration data set and 97.6% accuracy for wholesome birds and 96.0% accuracy for systemically diseased birds in the testing data set. By adjusting the classification threshold, 100% accuracy was achieved for systemically diseased birds with a decrease in accuracy for wholesome birds to 88.7%. This adjustment shows that the system can be feasibly adapted as needed for implementation for specific purposes, such as paw harvesting operations or prescreening for food safety inspection. This line-scan imaging system is ideal for directly implementing multispectral classification methods developed from hyperspectral image analysis.

  10. Time-resolved multispectral imaging of combustion reaction

    NASA Astrophysics Data System (ADS)

    Huot, Alexandrine; Gagnon, Marc-André; Jahjah, Karl-Alexandre; Tremblay, Pierre; Savary, Simon; Farley, Vincent; Lagueux, Philippe; Guyot, Éric; Chamberland, Martin; Marcotte, Fréderick

    2015-05-01

    Thermal infrared imaging is a field of science that evolves rapidly. Scientists have used for years the simplest tool: thermal broadband cameras. This allows to perform target characterization in both the longwave (LWIR) and midwave (MWIR) infrared spectral range. Infrared thermal imaging is used for a wide range of applications, especially in the combustion domain. For example, it can be used to follow combustion reactions, in order to characterize the injection and the ignition in a combustion chamber or even to observe gases produced by a flare or smokestack. Most combustion gases such as carbon dioxide (CO2) selectively absorb/emit infrared radiation at discrete energies, i.e. over a very narrow spectral range. Therefore, temperatures derived from broadband imaging are not reliable without prior knowledge about spectral emissivity. This information is not directly available from broadband images. However, spectral information is available using spectral filters. In this work, combustion analysis was carried out using Telops MS-IR MW camera which allows multispectral imaging at a high frame rate. A motorized filter wheel allowing synchronized acquisitions on eight (8) different channels was used to provide time-resolved multispectral imaging of combustion products of a candle in which black powder has been burnt to create a burst. It was then possible to estimate the temperature by modeling spectral profile derived from information obtained with the different spectral filters. Comparison with temperatures obtained using conventional broadband imaging illustrates the benefits of time-resolved multispectral imaging for the characterization of combustion processes.

  11. Time-resolved multispectral imaging of combustion reactions

    NASA Astrophysics Data System (ADS)

    Huot, Alexandrine; Gagnon, Marc-André; Jahjah, Karl-Alexandre; Tremblay, Pierre; Savary, Simon; Farley, Vincent; Lagueux, Philippe; Guyot, Éric; Chamberland, Martin; Marcotte, Frédérick

    2015-10-01

    Thermal infrared imaging is a field of science that evolves rapidly. Scientists have used for years the simplest tool: thermal broadband cameras. These allow to perform target characterization in both the longwave (LWIR) and midwave (MWIR) infrared spectral range. Infrared thermal imaging is used for a wide range of applications, especially in the combustion domain. For example, it can be used to follow combustion reactions, in order to characterize the injection and the ignition in a combustion chamber or even to observe gases produced by a flare or smokestack. Most combustion gases, such as carbon dioxide (CO2), selectively absorb/emit infrared radiation at discrete energies, i.e. over a very narrow spectral range. Therefore, temperatures derived from broadband imaging are not reliable without prior knowledge of spectral emissivity. This information is not directly available from broadband images. However, spectral information is available using spectral filters. In this work, combustion analysis was carried out using a Telops MS-IR MW camera, which allows multispectral imaging at a high frame rate. A motorized filter wheel allowing synchronized acquisitions on eight (8) different channels was used to provide time-resolved multispectral imaging of combustion products of a candle in which black powder has been burnt to create a burst. It was then possible to estimate the temperature by modeling spectral profiles derived from information obtained with the different spectral filters. Comparison with temperatures obtained using conventional broadband imaging illustrates the benefits of time-resolved multispectral imaging for the characterization of combustion processes.

  12. Classification images with uncertainty

    PubMed Central

    Tjan, Bosco S.; Nandy, Anirvan S.

    2009-01-01

    Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a “haze” that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms. PMID:16889477

  13. Classification images with uncertainty.

    PubMed

    Tjan, Bosco S; Nandy, Anirvan S

    2006-04-04

    Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a "haze" that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms.

  14. Multi-spectral confocal microendoscope for in-vivo imaging

    NASA Astrophysics Data System (ADS)

    Rouse, Andrew Robert

    The concept of in-vivo multi-spectral confocal microscopy is introduced. A slit-scanning multi-spectral confocal microendoscope (MCME) was built to demonstrate the technique. The MCME employs a flexible fiber-optic catheter coupled to a custom built slit-scan confocal microscope fitted with a custom built imaging spectrometer. The catheter consists of a fiber-optic imaging bundle linked to a miniature objective and focus assembly. The design and performance of the miniature objective and focus assembly are discussed. The 3mm diameter catheter may be used on its own or routed though the instrument channel of a commercial endoscope. The confocal nature of the system provides optical sectioning with 3mum lateral resolution and 30mum axial resolution. The prism based multi-spectral detection assembly is typically configured to collect 30 spectral samples over the visible chromatic range. The spectral sampling rate varies from 4nm/pixel at 490nm to 8nm/pixel at 660nm and the minimum resolvable wavelength difference varies from 7nm to 18nm over the same spectral range. Each of these characteristics are primarily dictated by the dispersive power of the prism. The MCME is designed to examine cellular structures during optical biopsy and to exploit the diagnostic information contained within the spectral domain. The primary applications for the system include diagnosis of disease in the gastro-intestinal tract and female reproductive system. Recent data from the grayscale imaging mode are presented. Preliminary multi-spectral results from phantoms, cell cultures, and excised human tissue are presented to demonstrate the potential of in-vivo multi-spectral imaging.

  15. The Multispectral Imaging Science Working Group. Volume 1: Executive summary

    NASA Technical Reports Server (NTRS)

    Cox, S. C. (Editor)

    1982-01-01

    Results of the deliberations of the six multispectral imaging science working groups (Botany, Geography, Geology, Hydrology, Imaging Science and Information Science) are summarized. Consideration was given to documenting the current state of knowledge in terrestrial remote sensing without the constraints of preconceived concepts such as possible band widths, number of bands, and radiometric or spatial resolutions of present or future systems. The findings of each working group included a discussion of desired capabilities and critical developmental issues.

  16. Real-time multispectral imaging application for poultry safety inspection

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Lawrence, Kurt C.; Windham, William R.; Snead, Matthew P.

    2006-02-01

    The ARS imaging research group in Athens, Georgia has developed a real-time multispectral imaging system for fecal and ingesta contaminant detection on broiler carcasses for poultry industry. The industrial scale system includes a common aperture camera with three visible wavelength optical trim filters. This paper demonstrates calibration of common aperture multispectral imaging hardware and real-time image processing software. The software design, especially the Unified Modeling Language (UML) design approach was used to develop real-time image processing software for on-line application. The UML models including class, object, activity, sequence, and collaboration diagram were presented. Both hardware and software for a real-time fecal and ingesta contaminant detection were tested at the pilot-scale poultry processing line. The test results of industrial sacle real-time system showed that the multispectral imaging technique performed well for detecting fecal contaminants with a commercial processing speed (currently 140 birds per minute). The accuracy for the detection of fecal and ingesta contaminates was approximately 96%.

  17. Enhancement of multispectral thermal infrared images - Decorrelation contrast stretching

    NASA Technical Reports Server (NTRS)

    Gillespie, Alan R.

    1992-01-01

    Decorrelation contrast stretching is an effective method for displaying information from multispectral thermal infrared (TIR) images. The technique involves transformation of the data to principle components ('decorrelation'), independent contrast 'stretching' of data from the new 'decorrelated' image bands, and retransformation of the stretched data back to the approximate original axes, based on the inverse of the principle component rotation. The enhancement is robust in that colors of the same scene components are similar in enhanced images of similar scenes, or the same scene imaged at different times. Decorrelation contrast stretching is reviewed in the context of other enhancements applied to TIR images.

  18. Retinal oxygen saturation evaluation by multi-spectral fundus imaging

    NASA Astrophysics Data System (ADS)

    Khoobehi, Bahram; Ning, Jinfeng; Puissegur, Elise; Bordeaux, Kimberly; Balasubramanian, Madhusudhanan; Beach, James

    2007-03-01

    Purpose: To develop a multi-spectral method to measure oxygen saturation of the retina in the human eye. Methods: Five Cynomolgus monkeys with normal eyes were anesthetized with intramuscular ketamine/xylazine and intravenous pentobarbital. Multi-spectral fundus imaging was performed in five monkeys with a commercial fundus camera equipped with a liquid crystal tuned filter in the illumination light path and a 16-bit digital camera. Recording parameters were controlled with software written specifically for the application. Seven images at successively longer oxygen-sensing wavelengths were recorded within 4 seconds. Individual images for each wavelength were captured in less than 100 msec of flash illumination. Slightly misaligned images of separate wavelengths due to slight eye motion were registered and corrected by translational and rotational image registration prior to analysis. Numerical values of relative oxygen saturation of retinal arteries and veins and the underlying tissue in between the artery/vein pairs were evaluated by an algorithm previously described, but which is now corrected for blood volume from averaged pixels (n > 1000). Color saturation maps were constructed by applying the algorithm at each image pixel using a Matlab script. Results: Both the numerical values of relative oxygen saturation and the saturation maps correspond to the physiological condition, that is, in a normal retina, the artery is more saturated than the tissue and the tissue is more saturated than the vein. With the multi-spectral fundus camera and proper registration of the multi-wavelength images, we were able to determine oxygen saturation in the primate retinal structures on a tolerable time scale which is applicable to human subjects. Conclusions: Seven wavelength multi-spectral imagery can be used to measure oxygen saturation in retinal artery, vein, and tissue (microcirculation). This technique is safe and can be used to monitor oxygen uptake in humans. This work

  19. Solid state high resolution multi-spectral imager CCD test phase

    NASA Technical Reports Server (NTRS)

    1973-01-01

    The program consisted of measuring the performance characteristics of charge coupled linear imaging devices, and a study defining a multispectral imaging system employing advanced solid state photodetection techniques.

  20. Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification.

    PubMed

    Gong, Wei; Sun, Jia; Shi, Shuo; Yang, Jian; Du, Lin; Zhu, Bo; Song, Shalei

    2015-09-02

    The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%-39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

  1. Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification.

    PubMed

    Gong, Wei; Sun, Jia; Shi, Shuo; Yang, Jian; Du, Lin; Zhu, Bo; Song, Shalei

    2015-01-01

    The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%-39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image. PMID:26340630

  2. Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification

    PubMed Central

    Gong, Wei; Sun, Jia; Shi, Shuo; Yang, Jian; Du, Lin; Zhu, Bo; Song, Shalei

    2015-01-01

    The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image. PMID:26340630

  3. Identification of atherosclerotic plaque components using cluster analysis of multispectral MR images: comparison with histology

    NASA Astrophysics Data System (ADS)

    Clarke, Sharon; Rutt, Brian K.; Lucas, Alexandra; Lownie, Stephen P.; Hammond, Robert; Mitchell, Ross

    2000-04-01

    The composition of atherosclerotic lesions in the carotid arteries is believed to be an important predictor of stroke risk. Several MR contrasts may be necessary to discriminate between different plaque components, and multispectral analysis can used to integrate the information obtained from these multiple contrasts. This study presents the use of registered MR and histological images of carotid endarterectomy specimens as a tool for the quantitative assessment of maximum likelihood classification and other segmentation algorithms. Carotid endarterectomy specimens were imaged in a 1.5T GE Signa scanner. PD, T1, T2, diffusion spin echo weightings were obtained. MR images were registered with digitized images of the corresponding histology. A pathologist identified regions of collagen, calcification, cholesterol, hemorrhage on the histological images. Training and ground truth regions were selected. The accuracy of the maximum likelihood classification was assessed on a pixel by pixel basis using truth regions identified on histological images. The accuracy of multispectral analysis was calcification (73%), fibrin (68%), cholesterol (62%), fibrous plaque (53%). This technique was limited by registration inaccuracies caused by partial volume effects and histological artifacts. Despite these limitations, accuracy results were reasonable. This technique, with continued improvements, provides a framework for evaluating the accuracy of different segmentation algorithms.

  4. Uniqueness in multispectral constant-wave epi-illumination imaging.

    PubMed

    Garcia-Allende, P B; Radrich, K; Symvoulidis, P; Glatz, J; Koch, M; Jentoft, K M; Ripoll, J; Ntziachristos, V

    2016-07-01

    Multispectral tissue imaging based on optical cameras and continuous-wave tissue illumination is commonly used in medicine and biology. Surprisingly, there is a characteristic absence of a critical look at the quantities that can be uniquely characterized from optically diffuse matter by multispectral imaging. Here, we investigate the fundamental question of uniqueness in epi-illumination measurements from turbid media obtained at multiple wavelengths. By utilizing an analytical model, tissue-mimicking phantoms, and an in vivo imaging experiment we show that independent of the bands employed, spectral measurements cannot uniquely retrieve absorption and scattering coefficients. We also establish that it is, nevertheless, possible to uniquely quantify oxygen saturation and the Mie scattering power-a previously undocumented uniqueness condition. PMID:27367111

  5. Acousto-optic tunable filter multispectral imaging system

    NASA Technical Reports Server (NTRS)

    Cheng, Li-Jen; Chao, Tien-Hsin; Reyes, George

    1992-01-01

    This paper discusses recent activities of Jet Propulsion Laboratory in the development of a new type of remote sensing multispectral imaging instruments using acousto-optic tunable filter (AOTF) as programmable bandpass filter. This remote sensor provides real-time operation; observational flexibility; measurements of spectral, spatial, and polarization information using a single instrument; and compact, solid state structure without moving parts. Two microcomputer-controlled AOTF imaging spectrometer breadboard systems were designed and built. One operates in the wavelength range of 0.48-0.76 micron and the other in the range of 1.2-2.5 micron. Experiments were performed using these two systems to observe geological and botanical objects in laboratory and outdoor environment. Results have demonstrated the feasibility of using the AOTF multispectral imaging system as a real-time versatile remote sensor with operational flexibility for future Army tactical applications.

  6. Quality assessment of butter cookies applying multispectral imaging.

    PubMed

    Andresen, Mette S; Dissing, Bjørn S; Løje, Hanne

    2013-07-01

    A method for characterization of butter cookie quality by assessing the surface browning and water content using multispectral images is presented. Based on evaluations of the browning of butter cookies, cookies were manually divided into groups. From this categorization, reference values were calculated for a statistical prediction model correlating multispectral images with a browning score. The browning score is calculated as a function of oven temperature and baking time. It is presented as a quadratic response surface. The investigated process window was the intervals 4-16 min and 160-200°C in a forced convection electrically heated oven. In addition to the browning score, a model for predicting the average water content based on the same images is presented. This shows how multispectral images of butter cookies may be used for the assessment of different quality parameters. Statistical analysis showed that the most significant wavelengths for browning predictions were in the interval 400-700 nm and the wavelengths significant for water prediction were primarily located in the near-infrared spectrum. The water prediction model was found to correctly estimate the average water content with an absolute error of 0.22%. From the images it was also possible to follow the browning and drying propagation from the cookie edge toward the center.

  7. Nanohole-array-based device for 2D snapshot multispectral imaging

    PubMed Central

    Najiminaini, Mohamadreza; Vasefi, Fartash; Kaminska, Bozena; Carson, Jeffrey J. L.

    2013-01-01

    We present a two-dimensional (2D) snapshot multispectral imager that utilizes the optical transmission characteristics of nanohole arrays (NHAs) in a gold film to resolve a mixture of input colors into multiple spectral bands. The multispectral device consists of blocks of NHAs, wherein each NHA has a unique periodicity that results in transmission resonances and minima in the visible and near-infrared regions. The multispectral device was illuminated over a wide spectral range, and the transmission was spectrally unmixed using a least-squares estimation algorithm. A NHA-based multispectral imaging system was built and tested in both reflection and transmission modes. The NHA-based multispectral imager was capable of extracting 2D multispectral images representative of four independent bands within the spectral range of 662 nm to 832 nm for a variety of targets. The multispectral device can potentially be integrated into a variety of imaging sensor systems. PMID:24005065

  8. Construction and demonstration of a multispectral tomographic scanning imager (TOSCA).

    PubMed

    Hovland, Harald

    2013-02-25

    This work presents the first experimental demonstrator of an imager based on a tomographic scanning (TOSCA) principle. The device described generates a stream of multispectral images of a scene or target using simple conical scan optics and a simple patterned reticle, followed by collecting optics and one or several single pixel detectors. Tomographic processing techniques are then applied to the one-dimensional signals to reproduce two-dimensional images. Various aspects of the design and construction are described, and resulting images and movies are shown. PMID:23482001

  9. Snapshot spectral and polarimetric imaging; target identification with multispectral video

    NASA Astrophysics Data System (ADS)

    Bartlett, Brent D.; Rodriguez, Mikel D.

    2013-05-01

    As the number of pixels continue to grow in consumer and scientific imaging devices, it has become feasible to collect the incident light field. In this paper, an imaging device developed around light field imaging is used to collect multispectral and polarimetric imagery in a snapshot fashion. The sensor is described and a video data set is shown highlighting the advantage of snapshot spectral imaging. Several novel computer vision approaches are applied to the video cubes to perform scene characterization and target identification. It is shown how the addition of spectral and polarimetric data to the video stream allows for multi-target identification and tracking not possible with traditional RGB video collection.

  10. Colorimetric-spectral clustering: a tool for multispectral image compression

    NASA Astrophysics Data System (ADS)

    Ciprian, R.; Carbucicchio, M.

    2011-11-01

    In this work a new compression method for multispectral images has been proposed: the 'colorimetric-spectral clustering'. The basic idea arises from the well-known cluster analysis, a multivariate analysis which finds the natural links between objects grouping them into clusters. In the colorimetric-spectral clustering compression method, the objects are the spectral reflectance factors of the multispectral images that are grouped into clusters on the basis of their colour difference. In particular two spectra can belong to the same cluster only if their colour difference is lower than a threshold fixed before starting the compression procedure. The performance of the colorimetric-spectral clustering has been compared to the k-means cluster analysis, in which the Euclidean distance between spectra is considered, to the principal component analysis and to the LabPQR method. The colorimetric-spectral clustering is able to preserve both the spectral and the colorimetric information of a multispectral image, allowing this information to be reproduced for all pixels of the image.

  11. Integration of visible/NIR spectroscopy and multispectral imaging for poultry carcass inspection

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Chen, Yud-Ren; Huffman, R. W.

    1995-01-01

    An integrated system which consisted of a visible/near-infrared spectroscopic subsystem and an intensified multispectral imaging subsystem was tested for its accuracy in separating abnormal (unwholesome) from normal poultry carcasses. The spectroscopic subsystem measured reflectance spectra of the poultry carcasses at wavelengths from 471 to 965 nm. For the multispectral imaging subsystem, the gray-level intensity of whole carcasses was measured using six different optical filters of 542, 571, 641, 700, 720, and 847 nm wavelengths. The preliminary results showed that, with the integrated system, there were no abnormal carcasses being misclassified as normal carcasses. When individual subsystem was used for classification, the error of the spectroscopic subsystem was 2.6% and that of the multispectral imaging subsystem was 3.9%. Thus, the integrated system could be used for separating carcasses into normal and abnormal streams. With perfect selection of normal carcasses in the normal carcass stream, the inspector needs only inspect the abnormal carcass stream. Thus, the through-put of carcasses of the processing line per inspector could be greatly increased.

  12. The Land Analysis System (LAS) for multispectral image processing

    USGS Publications Warehouse

    Wharton, S. W.; Lu, Y. C.; Quirk, Bruce K.; Oleson, Lyndon R.; Newcomer, J. A.; Irani, Frederick M.

    1988-01-01

    The Land Analysis System (LAS) is an interactive software system available in the public domain for the analysis, display, and management of multispectral and other digital image data. LAS provides over 240 applications functions and utilities, a flexible user interface, complete online and hard-copy documentation, extensive image-data file management, reformatting, conversion utilities, and high-level device independent access to image display hardware. The authors summarize the capabilities of the current release of LAS (version 4.0) and discuss plans for future development. Particular emphasis is given to the issue of system portability and the importance of removing and/or isolating hardware and software dependencies.

  13. Implementation of ILLIAC 4 algorithms for multispectral image interpretation. [earth resources data

    NASA Technical Reports Server (NTRS)

    Ray, R. M.; Thomas, J. D.; Donovan, W. E.; Swain, P. H.

    1974-01-01

    Research has focused on the design and partial implementation of a comprehensive ILLIAC software system for computer-assisted interpretation of multispectral earth resources data such as that now collected by the Earth Resources Technology Satellite. Research suggests generally that the ILLIAC 4 should be as much as two orders of magnitude more cost effective than serial processing computers for digital interpretation of ERTS imagery via multivariate statistical classification techniques. The potential of the ARPA Network as a mechanism for interfacing geographically-dispersed users to an ILLIAC 4 image processing facility is discussed.

  14. Classification images predict absolute efficiency.

    PubMed

    Murray, Richard F; Bennett, Patrick J; Sekuler, Allison B

    2005-02-24

    How well do classification images characterize human observers' strategies in perceptual tasks? We show mathematically that from the classification image of a noisy linear observer, it is possible to recover the observer's absolute efficiency. If we could similarly predict human observers' performance from their classification images, this would suggest that the linear model that underlies use of the classification image method is adequate over the small range of stimuli typically encountered in a classification image experiment, and that a classification image captures most important aspects of human observers' performance over this range. In a contrast discrimination task and in a shape discrimination task, we found that observers' absolute efficiencies were generally well predicted by their classification images, although consistently slightly (approximately 13%) higher than predicted. We consider whether a number of plausible nonlinearities can account for the slight under prediction, and of these we find that only a form of phase uncertainty can account for the discrepancy.

  15. Multispectral imaging contributions to global land ice measurements from space

    USGS Publications Warehouse

    Kargel, J.S.; Abrams, M.J.; Bishop, M.P.; Bush, A.; Hamilton, G.; Jiskoot, H.; Kaab, Andreas; Kieffer, H.H.; Lee, E.M.; Paul, F.; Rau, F.; Raup, B.; Shroder, J.F.; Soltesz, D.; Stainforth, D.; Stearns, L.; Wessels, R.

    2005-01-01

    Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world's glaciers, analyse them for glacier extent and changes, and assess change data for causes and implications for people and the environment. Although GLIMS is making use of multiple remote-sensing systems, ASTER (Advanced Spaceborne Thermal Emission and reflection Radiometer) is optimized for many needed observations, including mapping of glacier boundaries and material facies, and tracking of surface dynamics, such as flow vector fields and supraglacial lake development. Software development by GLIMS is geared toward mapping clean-ice and debris-covered glaciers; terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; multitemporal change analysis; visualization of images and derived data; and interpretation and archiving of derived data. A global glacier database has been designed at the National Snow and Ice Data Center (NSIDC, Boulder, Colorado); parameters are compatible with and expanded from those of the World Glacier Inventory (WGI). These technology efforts are summarized here, but will be presented in detail elsewhere. Our presentation here pertains to one broad question: How can ASTER and other satellite multispectral data be used to map, monitor, and characterize the state and dynamics of glaciers and to understand their responses to 20th and 21st century climate change? Our sampled results are not yet glaciologically or climatically representative. Our early results, while indicating complexity, are generally consistent with the glaciology community's conclusion that climate change is spurring glacier responses around the world (mainly retreat). Whether individual glaciers are advancing or retreating, the aggregate average of glacier change must be climatic in origin, as nonclimatic variations average out. We have discerned regional spatial patterns in glaciological response behavior

  16. Multiplexing with multispectral imaging: from mice to microscopy.

    PubMed

    Levenson, Richard M; Lynch, David T; Kobayashi, Hisataka; Backer, Joseph M; Backer, Marina V

    2008-01-01

    Increasing sophistication in the design and application of biological models as well as the advent of novel fluorescent probes have led to new demands on molecular imaging systems to deliver enhanced sensitivity, reliable quantitation, and the ability to resolve multiple simultaneous signals. Sensitivity is limited, especially in the visible spectral range, by the presence of ubiquitous autofluorescence signals (mostly arising from the skin and gut), which need to be separated from those of targeted fluorophores. Fluorescence-based imaging is also affected by absorbing and scattering properties of tissue in both the visible and to a lesser extent the near-infrared (NIR) regions. However, the small size of typical animal models (usually mice) often permits the detection of enough light arising even from relatively deep locations to allow the capture of signals with an acceptable signal-to-noise ratio. Multispectral imaging, through its ability to separate autofluorescence from label fluorescence, can increase sensitivity as much as 300 times compared to conventional approaches, and concomitantly improve quantitative accuracy. In the NIR region, autofluorescence, while still significant, poses less of a problem. However, the task of disentangling signals from multiple fluorophores remains. Multispectral imaging allows the separation of five or more fluorophores, with each signal quantitated and visualized separately. Preclinical small animal imaging is often accompanied by microscopic analysis, both before and after the in vivo phase. This can involve tissue culture manipulations and/or histological examination of fixed or frozen tissue. Due to the same advantages in sensitivity, quantitation, and multiplexing, microscopy-based multispectral techniques form an excellent complement to in vivo imaging.

  17. Multispectral Thermal Imager (MTI) Payload Overview

    SciTech Connect

    Bender, S.C.; Brock, B.C.; Bullington, D.M.; Byrd, D.A.; Claassen, P.J.; Decker, M.L.; Henson, T.D.; Kay, R.R.; Kidner, R.E.; Lanes, C.E.; Little, C.; Marbach, K.D.; Rackley, N.G.; Rienstra, J.L.; Smith, B.W.; Taplin, R.B.; Weber, P.G.

    1999-07-07

    MTI is a comprehensive research and development project that includes up-front modeling and analysis, satellite system design, fabrication, assembly and testing, on-orbit operations, and experimentation and data analysis. The satellite is designed to collect radiometrically calibrated, medium resolution imagery in 15 spectral bands ranging from 0.45 to 10.70 pm. The payload portion of the satellite includes the imaging system components, associated electronics boxes, and payload support structure. The imaging system includes a three-mirror anastigmatic off-axis telescope, a single cryogenically cooled focal plane assembly, a mechanical cooler, and an onboard calibration system. Payload electronic subsystems include image digitizers, real-time image compressors, a solid state recorder, calibration source drivers, and cooler temperature and vibration controllers. The payload support structure mechanically integrates all payload components and provides a simple four point interface to the spacecraft bus. All payload components have been fabricated and tested, and integrated.

  18. Implementation and evaluation of ILLIAC 4 algorithms for multispectral image processing

    NASA Technical Reports Server (NTRS)

    Swain, P. H.

    1974-01-01

    Data concerning a multidisciplinary and multi-organizational effort to implement multispectral data analysis algorithms on a revolutionary computer, the Illiac 4, are reported. The effectiveness and efficiency of implementing the digital multispectral data analysis techniques for producing useful land use classifications from satellite collected data were demonstrated.

  19. Video rate multispectral imaging for camouflaged target detection

    NASA Astrophysics Data System (ADS)

    Henry, Sam

    2015-05-01

    The ability to detect and identify camouflaged targets is critical in combat environments. Hyperspectral and Multispectral cameras allow a soldier to identify threats more effectively than traditional RGB cameras due to both increased color resolution and ability to see beyond visible light. Static imagers have proven successful, however the development of video rate imagers allows for continuous real time target identification and tracking. This paper presents an analysis of existing anomaly detection algorithms and how they can be adopted to video rates, and presents a general purpose semisupervised real time anomaly detection algorithm using multiple frame sampling.

  20. Analysis of lithology: Vegetation mixes in multispectral images

    NASA Technical Reports Server (NTRS)

    Adams, J. B.; Smith, M.; Adams, J. D.

    1982-01-01

    Discrimination and identification of lithologies from multispectral images is discussed. Rock/soil identification can be facilitated by removing the component of the signal in the images that is contributed by the vegetation. Mixing models were developed to predict the spectra of combinations of pure end members, and those models were refined using laboratory measurements of real mixtures. Models in use include a simple linear (checkerboard) mix, granular mixing, semi-transparent coatings, and combinations of the above. The use of interactive computer techniques that allow quick comparison of the spectrum of a pixel stack (in a multiband set) with laboratory spectra is discussed.

  1. Component pattern analysis of chemicals using multispectral THz imaging system

    NASA Astrophysics Data System (ADS)

    Kawase, Kodo; Ogawa, Yuichi; Watanabe, Yuki

    2004-04-01

    We have developed a novel basic technology for terahertz (THz) imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral transillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.

  2. Recognition of lineaments in Eastern Rhodopes on Landsat multispectral images

    NASA Astrophysics Data System (ADS)

    Borisova, Denitsa; Jelev, Georgi; Atanassov, Valentin; Koprinkova-Hristova, Petia; Alexiev, Kiril

    Lineaments usually appear on the multispectral images as lines (edges) or linear formations as a result of the color variations of the surface structures. Lineaments are line features on earth’s surface which reflect geological structure. The basic geometry of a line is orientation, length and curve. Detection of lineaments is an important operation in the exploration for mineral deposits, in the investigation of active fault patterns, water resources, etc. In this study the integrated approach is applied. It comes together the methods of the visual interpretation of various geological and geographical indications in the satellite images, application of spatial analysis in GIS and automatic processing of Landsat multispectral image by Canny algorithm, Directional Filter and Neural Network. Canny algorithm for extracting edges is series of filters (Gaussian, Sobel, etc.) applied to all bands of the image using the free IDL source (http://www.cis.rit.edu/class/simg782/programs/ canny.pro). Directional Filter is applied to sharpen the image in a specific (preferred) direction. Another method is the Neural Network algorithm for recognizing lineaments. Lineaments are effectively extracted using different methods of automatic. The results from above mentioned methods are compared to results derived from visual interpretation of satellite images and from geological map. The rose-diagrams of distribution of lineaments and maps of their density are completed. Acknowledgments: This study is supported by the project DFNI - I01/8 funded by the Bulgarian Science Fund.

  3. Effect of Pansharpened Image on Some of Pixel Based and Object Based Classification Accuracy

    NASA Astrophysics Data System (ADS)

    Karakus, P.; Karabork, H.

    2016-06-01

    Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

  4. Algorithms for lineaments detection in processing of multispectral images

    NASA Astrophysics Data System (ADS)

    Borisova, D.; Jelev, G.; Atanassov, V.; Koprinkova-Hristova, Petia; Alexiev, K.

    2014-10-01

    Satellite remote sensing is a universal tool to investigate the different areas of Earth and environmental sciences. The advancement of the implementation capabilities of the optoelectronic devices which are long-term-tested in the laboratory and the field and are mounted on-board of the remote sensing platforms further improves the capability of instruments to acquire information about the Earth and its resources in global, regional and local scales. With the start of new high-spatial and spectral resolution satellite and aircraft imagery new applications for large-scale mapping and monitoring becomes possible. The integration with Geographic Information Systems (GIS) allows a synergistic processing of the multi-source spatial and spectral data. Here we present the results of a joint project DFNI I01/8 funded by the Bulgarian Science Fund focused on the algorithms of the preprocessing and the processing spectral data by using the methods of the corrections and of the visual and automatic interpretation. The objects of this study are lineaments. The lineaments are basically the line features on the earth's surface which are a sign of the geological structures. The geological lineaments usually appear on the multispectral images like lines or edges or linear shapes which is the result of the color variations of the surface structures. The basic geometry of a line is orientation, length and curve. The detection of the geological lineaments is an important operation in the exploration for mineral deposits, in the investigation of active fault patterns, in the prospecting of water resources, in the protecting people, etc. In this study the integrated approach for the detecting of the lineaments is applied. It combines together the methods of the visual interpretation of various geological and geographical indications in the multispectral satellite images, the application of the spatial analysis in GIS and the automatic processing of the multispectral images by Canny

  5. Multi-spectral image dissector camera system

    NASA Technical Reports Server (NTRS)

    1972-01-01

    The image dissector sensor for the Earth Resources Program is evaluated using contrast and reflectance data. The ground resolution obtainable for low contrast at the targeted signal to noise ratio of 1.8 was defined. It is concluded that the system is capable of achieving the detection of small, low contrast ground targets from satellites.

  6. Morphological Feature Extraction for Automatic Registration of Multispectral Images

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2007-01-01

    The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

  7. Dual plasmonic gold nanoparticles for multispectral photoacoustic imaging application

    NASA Astrophysics Data System (ADS)

    Raghavan, Vijay; Subhash, Hrebesh; Breathnach, Aedán.; Leahy, Martin; Dockery, Peter; Olivo, Malini

    2014-03-01

    Nanoparticle contrast agents for molecular targeted imaging have widespread interest in diagnostic applications with cellular resolution, specificity and selectivity for visualization and assessment of various disease processes. Of particular interest is gold nanoparticle owing to its tunability of the surface plasmon resonance (SPR) and its relative inertness. Here we present the synthesis of anisotropic multi-branched star shaped gold nanoparticles exhibiting dual-band plasmon absorption peaks and its application as a contrast agent for multispectral photoacoustic imaging. The transverse plasmon absorption peak of the synthesised dual plasmonic gold nanostar (DPGNS) was around 700 nm and that of longitudinal plasmon absorption in the longer wavelength region around 1050-1150 nm. Unlike most reported PA contrast agent with surface plasmon absorption in the range of 700 to 800 nm showing moderate tissue penetration, 1050-1200 nm range lies in the farther region of the optical window of biological tissue where scattering and the intrinsic optical extinction of endogenous chromophores is at its minimum. We also present a proof of principle demonstration of DPGNS as contrast agent for multispectral photoacoustic animal imaging. Our results show that DPGNS are promising for PA imaging with extended-depth imaging applications.

  8. Online quantitative analysis of multispectral images of human body tissues

    SciTech Connect

    Lisenko, S A

    2013-08-31

    A method is developed for online monitoring of structural and morphological parameters of biological tissues (haemoglobin concentration, degree of blood oxygenation, average diameter of capillaries and the parameter characterising the average size of tissue scatterers), which involves multispectral tissue imaging, image normalisation to one of its spectral layers and determination of unknown parameters based on their stable regression relation with the spectral characteristics of the normalised image. Regression is obtained by simulating numerically the diffuse reflectance spectrum of the tissue by the Monte Carlo method at a wide variation of model parameters. The correctness of the model calculations is confirmed by the good agreement with the experimental data. The error of the method is estimated under conditions of general variability of structural and morphological parameters of the tissue. The method developed is compared with the traditional methods of interpretation of multispectral images of biological tissues, based on the solution of the inverse problem for each pixel of the image in the approximation of different analytical models. (biomedical optics)

  9. Portable multispectral imaging system for oral cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Hsieh, Yao-Fang; Ou-Yang, Mang; Lee, Cheng-Chung

    2013-09-01

    This study presents the portable multispectral imaging system that can acquire the image of specific spectrum in vivo for oral cancer diagnosis. According to the research literature, the autofluorescence of cells and tissue have been widely applied to diagnose oral cancer. The spectral distribution is difference for lesions of epithelial cells and normal cells after excited fluorescence. We have been developed the hyperspectral and multispectral techniques for oral cancer diagnosis in three generations. This research is the third generation. The excited and emission spectrum for the diagnosis are acquired from the research of first generation. The portable system for detection of oral cancer is modified for existing handheld microscope. The UV LED is used to illuminate the surface of oral cavity and excite the cells to produce fluorescent. The image passes through the central channel and filters out unwanted spectrum by the selection of filter, and focused by the focus lens on the image sensor. Therefore, we can achieve the specific wavelength image via fluorescence reaction. The specificity and sensitivity of the system are 85% and 90%, respectively.

  10. Multispectral Chiral Imaging with a Metalens.

    PubMed

    Khorasaninejad, M; Chen, W T; Zhu, A Y; Oh, J; Devlin, R C; Rousso, D; Capasso, F

    2016-07-13

    The vast majority of biologically active compounds, ranging from amino acids to essential nutrients such as glucose, possess intrinsic handedness. This in turn gives rise to chiral optical properties that provide a basis for detecting and quantifying enantio-specific concentrations of these molecules. However, traditional chiroptical spectroscopy and imaging techniques require cascading of multiple optical components in sophisticated setups. Here, we present a planar lens with an engineered dispersive response, which simultaneously forms two images with opposite helicity of an object within the same field-of-view. In this way, chiroptical properties can be probed across the visible spectrum using only the lens and a camera without the addition of polarizers or dispersive optical devices. We map the circular dichroism of the exoskeleton of a chiral beetle, Chrysina gloriosa, which is known to exhibit high reflectivity of left-circularly polarized light, with high spatial resolution limited by the numerical aperture of the planar lens. Our results demonstrate the potential of metasurfaces in realizing a compact and multifunctional device with unprecedented imaging capabilities. PMID:27267137

  11. Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Cao, Le-lin; Wu, Chun-feng; Hou, Qing-yu

    2009-07-01

    A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital simulation experiment result verifies that the algorithm is effective and feasible.

  12. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  13. Computer implemented classification of vegetation using aircraft acquired multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Cibula, W. G.

    1975-01-01

    The use of aircraft 24-channel multispectral scanner data in conjunction with computer processing techniques to obtain an automated classification of plant species association was discussed. The classification of various plant species associations was related to information needed for specific applications. In addition, the necessity for multiple selection of training fields for a single class in situations where the study area consists of highly irregular terrain was detailed. A single classification was illuminated differently in different areas, resulting in the existence of multiple spectral signatures for a given class. These different signatures result since different qualities of radiation upwell to the detector from portions that have differing qualities of incident radiation. Techniques of training field selection were outlined, and a classification obtained from a natural area in Tishomingo State Park in northern Mississippi was presented.

  14. Modeling space-based multispectral imaging systems with DIRSIG

    NASA Astrophysics Data System (ADS)

    Brown, Scott D.; Sanders, Niek J.; Goodenough, Adam A.; Gartley, Michael

    2011-06-01

    The Landsat Data Continuity Mission (LDCM) focuses on a next generation global coverage, imaging system to replace the aging Landsat 5 and Landsat 7 systems. The major difference in the new system is the migration from the multi-spectral whiskbroom design employed by the previous generation of sensors to modular focal plane, multi-spectral pushbroom architecture. Further complicating the design shift is that the reflective and thermal acquisition capability is split across two instruments spatially separated on the satellite bus. One of the focuses of the science and engineering teams prior to launch is the ability to provide seamless data continuity with the historic Landsat data archive. Specifically, the challenges of registering and calibrating data from the new system so that long-term science studies are minimally impacted by the change in the system design. In order to provide the science and engineering teams with simulated pre-launch data, an effort was undertaken to create a robust end-to-end model of the LDCM system. The modeling environment is intended to be flexible and incorporate measured data from the actual system components as they were completed and integrated. The output of the modeling environment needs to include not only radiometrically robust imagery, but also the meta-data necessary to exercise the processing pipeline. This paper describes how the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been utilized to model space-based, multi-spectral imaging (MSI) systems in support of systems engineering trade studies. A mechanism to incorporate measured focal plane projections through the forward optics is described. A hierarchal description of the satellite system is presented including the details of how a multiple instrument platform is described and modeled, including the hierarchical management of temporally correlated jitter that allows engineers to explore impacts of different jitter sources on instrument

  15. Compact multispectral imaging system for contaminant detection on poultry carcass

    NASA Astrophysics Data System (ADS)

    Kise, Michio; Park, Bosoon; Lawrence, Kurt C.; Windham, William R.

    2007-02-01

    The objective of this research was to design and fabricate a compact, cost effective multispectral instrument and to collect and analyze spectra for real-time contaminant detection for poultry processing plants. The prototype system developed in this research consisted of a multispectral imaging system, illumination system and an industrial portable computer. The dual-band spectral imaging system developed in this study was a two-port imaging system that consisted of two identical monochrome cameras, optical system and two narrow bandpass filters whose center of the wavelength are 520 and 560 nm with 10 nm FWHM, respectively. A spectral reflectance from a chicken carcass was collected and split in two directions by an optical system including a beamsplitter and lenses, and then two identical collimated lights were filtered by the narrow bandpass filters and delivered to the cameras. Lens distortions and geometric misalignment of the two cameras were mathematically corrected. The prototype system was tested at the real-time processing line and the preliminary results showed that the dual-band spectral imaging system could effectively detect feces and ingesta on the surface of poultry carcass.

  16. Multispectral image processing: the nature factor

    NASA Astrophysics Data System (ADS)

    Watkins, Wendell R.

    1998-09-01

    The images processed by our brain represent our window into the world. For some animals this window is derived from a single eye, for others, including humans, two eyes provide stereo imagery, for others like the black widow spider several eyes are used (8 eyes), and some insects like the common housefly utilize thousands of eyes (ommatidia). Still other animals like the bat and dolphin have eyes for regular vision, but employ acoustic sonar vision for seeing where their regular eyes don't work such as in pitch black caves or turbid water. Of course, other animals have adapted to dark environments by bringing along their own lighting such as the firefly and several creates from the depths of the ocean floor. Animal vision is truly varied and has developed over millennia in many remarkable ways. We have learned a lot about vision processes by studying these animal systems and can still learn even more.

  17. Multispectral therapeutic endoscopy imaging and intervention

    NASA Astrophysics Data System (ADS)

    Bala, John L.; Schwaitzberg, Steven D.

    2007-02-01

    With the debut of antibiotic drug therapy, and as a result of its ease of use and general success in treating infection, drugs have become the treatment of choice for most bacterial infections. However, the advent of multiple, very aggressive drug-resistant bacteria, an increasing population which cannot tolerate drugs, and the high cost of drug therapy suggest that a new modality for treating infections is needed. The complex interplay of clonal spread, persistence, transfer of resistance elements and cell-to-cell interaction all contribute to the difficulty in developing drugs to treat new antibiotic-resistant bacterial strains. A dynamic non-drug system, using extant pulsed ultraviolet lightwave technology to kill infection, is being developed to destroy pathogens. This paper theorizes that the shock effect of pulsed xenon's high energy ultraviolet pulses at wavelengths between 250-270nm separates the bacteria's DNA bands, and, subsequently, destroys them. Preliminary laboratory tests have demonstrated the ability of the technology to destroy Staphylococcus aureus, Pseudomonas aeruginosa Escherichia coli, Helicobacter pylori, Acinetobacter baumannii, Klebsiella punemonia, Bacillus subtillis, and Aspergillus fumigates at penetration depths of greater than 3mm in fluids with 100% effectiveness in less than five seconds of exposure to pulsed xenon lightwaves. Micro Invasive Technology, Inc is developing .pulsed xenon therapeutic catheters and endoscopic instruments for internal antimicrobial eradication and topographical devices for prophylactic wound, burn and surgical entrance/exit site sterilization. Pulsed Xenon light sources have a broad optical spectrum (190-1200nm), and can generate light pulses with sufficient energy for combined imaging and therapeutic intervention by multiplexing a fiber optic pathway into the body. In addition, Pulsed Xenon has proven ability to activate photo reactive dyes; share endoscopic lightguides with lasers while, simultaneously

  18. A real-time multispectral imaging system for low- or mid-altitude remote sensing

    NASA Astrophysics Data System (ADS)

    Yi, Dingrong; Kong, Linghua

    2012-10-01

    Multispectral imaging is a powerful tool in remote sensing applications. Recently a micro-arrayed narrow-band optical mosaic filter was invented and successfully fabricated to reduce the size and cost of multispectral imaging devices in order to meet the requirements for low- or mid- altitude remote sensing. Such a filter with four narrow bands is integrated with an off-shelf CCD camera, resulting in an economic and light-weight multispectral imaging camera with the capacity of producing multiple images at different center wavelengths with a single shot. The multispectral imaging camera is then integrated with a wireless transmitter and battery to produce a remote sensing multispectral imaging system. The design and some preliminary results of a prototyped multispectral imaging system with the potential for remote sensing applications with a weight of only 200 grams are reported. The prototyped multispectral imaging system eliminates the image registration procedure required by traditional multispectral imaging technologies. In addition, it has other advantages such as low cost, being light weight and compact in design.

  19. Monitoring human melanocytic cell responses to piperine using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Samatham, Ravikant; Phillips, Kevin G.; Sonka, Julia; Yelma, Aznegashe; Reddy, Neha; Vanka, Meenakshi; Thuillier, Philippe; Soumyanath, Amala; Jacques, Steven

    2011-03-01

    Vitiligo is a depigmentary disease characterized by melanocyte loss attributed most commonly to autoimmune mechanisms. Currently vitiligo has a high incidence (1% worldwide) but a poor set of treatment options. Piperine, a compound found in black pepper, is a potential treatment for the depigmentary skin disease vitiligo, due to its ability to stimulate mouse epidermal melanocyte proliferation in vitro and in vivo. The present study investigates the use of multispectral imaging and an image processing technique based on local contrast to quantify the stimulatory effects of piperine on human melanocyte proliferation in reconstructed epidermis. We demonstrate the ability of the imaging method to quantify increased pigmentation in response to piperine treatment. The quantization of melanocyte stimulation by the proposed imaging technique illustrates the potential use of this technology to quickly assess therapeutic responses of vitiligo tissue culture models to treatment non-invasively.

  20. Digital enhancement of multispectral MSS data for maximum image visibility

    NASA Technical Reports Server (NTRS)

    Algazi, V. R.

    1973-01-01

    A systematic approach to the enhancement of images has been developed. This approach exploits two principal features involved in the observation of images: the properties of human vision and the statistics of the images being observed. The rationale of the enhancement procedure is as follows: in the observation of some features of interest in an image, the range of objective luminance-chrominance values being displayed is generally limited and does not use the whole perceptual range of vision of the observer. The purpose of the enhancement technique is to expand and distort in a systematic way the grey scale values of each of the multispectral bands making up a color composite, to enhance the average visibility of the features being observed.

  1. Jovian Chromophore Characteristics from Multispectral HST Images

    NASA Technical Reports Server (NTRS)

    Strycker, Paul D.; Chanover, Nancy J.; Simon-Miller, Amy A.; Banfield, Don; Gierasch, Peter J.

    2011-01-01

    The chromophores responsible for coloring the jovian atmosphere are embedded within Jupiter's vertical aerosol structure. Sunlight propagates through this vertical distribution of aerosol particles, whose colors are defined by omega-bar (sub 0)(lambda), and we remotely observe the culmination of the radiative transfer as I/F(lambda). In this study, we employed a radiative transfer code to retrieve omega-bar (sub 0)(lambda) for particles in Jupiter's tropospheric haze at seven wavelengths in the near-UV and visible regimes. The data consisted of images of the 2008 passage of Oval BA to the south of the Great Red Spot obtained by the Wide Field Planetary Camera 2 on-board the Hubble Space Telescope. We present derived particle colors for locations that were selected from 14 weather regions, which spanned a large range of observed colors. All omega-bar (sub 0)(lambda) curves were absorbing in the blue, and omega-bar (sub 0)(lambda) increased monotonically to approximately unity as wavelength increased. We found accurate fits to all omega-bar (sub 0)(lambda) curves using an empirically derived functional form: omega-bar (sub 0)(lambda) = 1 A exp(-B lambda). The best-fit parameters for the mean omega-bar (sub 0)(lambda) curve were A = 25.4 and B = 0.0149 for lambda in units of nm. We performed a principal component analysis (PCA) on our omega-bar (sub 0)(lambda) results and found that one or two independent chromophores were sufficient to produce the variations in omega-bar (sub 0)(lambda). A PCA of I/F(lambda) for the same jovian locations resulted in principal components (PCs) with roughly the same variances as the omega-bar (sub 0)(lambda) PCA, but they did not result in a one-to-one mapping of PC amplitudes between the omega-bar (sub 0)(lambda) PCA and I/F(lambda) PCA. We suggest that statistical analyses performed on I/ F(lambda) image cubes have limited applicability to the characterization of chromophores in the jovian atmosphere due to the sensitivity of 1/ F

  2. An automatic agricultural zone classification procedure for crop inventory satellite images

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Kux, H. J.; Velasco, F. R. D.; Deoliveira, M. O. B.

    1982-01-01

    A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.

  3. Multimodal tissue perfusion imaging using multi-spectral and thermographic imaging systems applied on clinical data

    NASA Astrophysics Data System (ADS)

    Klaessens, John H. G. M.; Nelisse, Martin; Verdaasdonk, Rudolf M.; Noordmans, Herke Jan

    2013-03-01

    Clinical interventions can cause changes in tissue perfusion, oxygenation or temperature. Real-time imaging of these phenomena could be useful for surgical strategy or understanding of physiological regulation mechanisms. Two noncontact imaging techniques were applied for imaging of large tissue areas: LED based multispectral imaging (MSI, 17 different wavelengths 370 nm-880 nm) and thermal imaging (7.5 to 13.5 μm). Oxygenation concentration changes were calculated using different analyzing methods. The advantages of these methods are presented for stationary and dynamic applications. Concentration calculations of chromophores in tissue require right choices of wavelengths The effects of different wavelength choices for hemoglobin concentration calculations were studied in laboratory conditions and consequently applied in clinical studies. Corrections for interferences during the clinical registrations (ambient light fluctuations, tissue movements) were performed. The wavelength dependency of the algorithms were studied and wavelength sets with the best results will be presented. The multispectral and thermal imaging systems were applied during clinical intervention studies: reperfusion of tissue flap transplantation (ENT), effectiveness of local anesthetic block and during open brain surgery in patients with epileptic seizures. The LED multispectral imaging system successfully imaged the perfusion and oxygenation changes during clinical interventions. The thermal images show local heat distributions over tissue areas as a result of changes in tissue perfusion. Multispectral imaging and thermal imaging provide complementary information and are promising techniques for real-time diagnostics of physiological processes in medicine.

  4. Improving multispectral satellite image compression using onboard subpixel registration

    NASA Astrophysics Data System (ADS)

    Albinet, Mathieu; Camarero, Roberto; Isnard, Maxime; Poulet, Christophe; Perret, Jokin

    2013-09-01

    Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression. In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

  5. A Multispectral Micro-Imager for Lunar Field Geology

    NASA Technical Reports Server (NTRS)

    Nunez, Jorge; Farmer, Jack; Sellar, Glenn; Allen, Carlton

    2009-01-01

    Field geologists routinely assign rocks to one of three basic petrogenetic categories (igneous, sedimentary or metamorphic) based on microtextural and mineralogical information acquired with a simple magnifying lens. Indeed, such observations often comprise the core of interpretations of geological processes and history. The Multispectral Microscopic Imager (MMI) uses multi-wavelength, light-emitting diodes (LEDs) and a substrate-removed InGaAs focal-plane array to create multispectral, microscale reflectance images of geological samples (FOV 32 X 40 mm). Each pixel (62.5 microns) of an image is comprised of 21 spectral bands that extend from 470 to 1750 nm, enabling the discrimination of a wide variety of rock-forming minerals, especially Fe-bearing phases. MMI images provide crucial context information for in situ robotic analyses using other onboard analytical instruments (e.g. XRD), or for the selection of return samples for analysis in terrestrial labs. To further assess the value of the MMI as a tool for lunar exploration, we used a field-portable, tripod-mounted version of the MMI to image a variety of Apollo samples housed at the Lunar Experiment Laboratory, NASA s Johnson Space Center. MMI images faithfully resolved the microtextural features of samples, while the application of ENVI-based spectral end member mapping methods revealed the distribution of Fe-bearing mineral phases (olivine, pyroxene and magnetite), along with plagioclase feldspars within samples. Samples included a broad range of lithologies and grain sizes. Our MMI-based petrogenetic interpretations compared favorably with thin section-based descriptions published in the Lunar Sample Compendium, revealing the value of MMI images for astronaut and rover-mediated lunar exploration.

  6. Two Levels Fusion Decision for Multispectral Image Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Elmannai, H.; Loghmari, M. A.; Naceur, M. S.

    2015-10-01

    Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition approach. Source separation, feature extraction and decisional fusion are the main stages to establish an automatic pattern recognizer. The first stage is pre-processing and is based on non linear source separation. The mixing process is considered non linear with gaussians distributions. The second stage performs feature extraction for Gabor, Wavelet and Curvelet transform. Feature information presentation provides an efficient information description for machine vision projects. The third stage is a decisional fusion performed in two steps. The first step assign the best feature to each source/pattern using the accuracy matrix obtained from the learning data set. The second step is a source majority vote. Classification is performed by Support Vector Machine. Experimentation results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for pattern recognition.

  7. An application of LANDSAT multispectral imagery for the classification of hydrobiological systems, Shark River Slough, Everglades National Park, Florida

    NASA Technical Reports Server (NTRS)

    Rose, P. W.; Rosendahl, P. C. (Principal Investigator)

    1979-01-01

    Multivariant hydrologic parameters over the Shark River Slough were investigated. Ground truth was established utilizing U-2 infrared photography and comprehensive field data to define a control network which represented all hydrobiological systems in the slough. These data were then applied to LANDSAT imagery utilizing an interactive multispectral processor which generated hydrographic maps through classification of the slough and defined the multispectral surface radiance characteristics of the wetlands areas in the park. The spectral response of each hydrobiological zone was determined and plotted to formulate multispectral relationships between the emittent energy from the slough in order to determine the best possible multispectral wavelength combinations to enhance classification results. The extent of each hydrobiological zone in slough was determined and flow vectors for water movement throughout the slough established.

  8. Multispectral information hiding in RGB image using bit-plane-based watermarking and its application

    NASA Astrophysics Data System (ADS)

    Shinoda, Kazuma; Watanabe, Aya; Hasegawa, Madoka; Kato, Shigeo

    2015-06-01

    Although it was expected that multispectral images would be implemented in many applications, such as remote sensing and medical imaging, their use has not been widely diffused in these fields. The development of a compact multispectral camera and display will be needed for practical use, but the format compatibility between multispectral and RGB images is also important for reducing the introduction cost and having high usability. We propose a method of embedding the spectral information into an RGB image by watermarking. The RGB image is calculated from the multispectral image, and then, the original multispectral image is estimated from the RGB image using Wiener estimation. The residual data between the original and the estimated multispectral image are compressed and embedded in the lower bit planes of the RGB image. The experimental results show that, as compared with Wiener estimation, the proposed method leads to more than a 10 dB gain in the peak signal-to-noise ratio of the reconstructed multispectral image, while there are almost no significant perceptual differences in the watermarked RGB image.

  9. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

  10. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data. PMID:17063681

  11. Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging

    NASA Astrophysics Data System (ADS)

    Chaudhari, Abhijit J.; Darvas, Felix; Bading, James R.; Moats, Rex A.; Conti, Peter S.; Smith, Desmond J.; Cherry, Simon R.; Leahy, Richard M.

    2005-12-01

    For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour.

  12. Ice Cloud Optical Depth Retrievals from CRISM Multispectral Images

    NASA Astrophysics Data System (ADS)

    Klassen, David R.

    2014-11-01

    One set of data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on the Mars Reconnaissance Orbiter (MRO) is the multispectral survey that measured the visible-through-near-infrared reflectance of the entire planet of Mars at specific wavelengths. The spectral data from several sols were be combined to create multi-spectral maps of Mars. In addition, these maps can be zonally averaged to create a latitude vs season image cube of Mars. All of these image cubes can be fit using a full radiative transfer modeling in order to retrieve ice cloud optical depth—as a map for one of the particular dates, or as a latitude vs season record.To compare the data radiative transfer models, a measure of the actual surface reflectance is needed. There are several possible ways to model this, such as using a nearby region that is "close enough" or by looking at the same region at different times and assuming one of those is the actual surface reflectance. Neither of these is ideal for trying to process an entire map of data because aerosol clouds can be fairly extensive both spatially and temporally.Another technique is to assume that the surface can be modeled as a linear combination of a limited set of intrinsic spectral endmembers. A combination of Principal Component Analysis (PCA) and Target Transformation (TT) has been used to recover just such a set of spectral endmember shapes. The coefficients in the linear combination then become additional fitting parameters in the radiative transfer modeling of each map point—all parameters are adjusted until the RMS error between the model and the data is minimized. Based on previous work, the PCA of martian spectral image cubes is relatively consistent regardless of season, implying the underlying, large-scale, intrinsic traits that dominate the data variance are relatively constant. These overall PCA results can then be used to create a single set of spectral endmembers that can be used for any of the data

  13. Optical perception for detection of cutaneous T-cell lymphoma by multi-spectral imaging

    NASA Astrophysics Data System (ADS)

    Hsiao, Yu-Ping; Wang, Hsiang-Chen; Chen, Shih-Hua; Tsai, Chung-Hung; Yang, Jen-Hung

    2014-12-01

    In this study, the spectrum of each picture element of the patient’s skin image was obtained by multi-spectral imaging technology. Spectra of normal or pathological skin were collected from 15 patients. Principal component analysis and principal component scores of skin spectra were employed to distinguish the spectral characteristics with different diseases. Finally, skin regions with suspected cutaneous T-cell lymphoma (CTCL) lesions were successfully predicted by evaluation and classification of the spectra of pathological skin. The sensitivity and specificity of this technique were 89.65% and 95.18% after the analysis of about 109 patients. The probability of atopic dermatitis and psoriasis patients misinterpreted as CTCL were 5.56% and 4.54%, respectively.

  14. Classification of non native tree species in Adda Park (Italy) through multispectral and multitemporal surveys from UAV

    NASA Astrophysics Data System (ADS)

    Pinto, Livio; Sona, Giovanna; Biffi, Andrea; Dosso, Paolo; Passoni, Daniele; Baracani, Matteo

    2014-05-01

    July, was realized over a longer period : from 09/07/2013 to 28/08/2013, due to weather condition and technical reasons. In any case the vegetation characteristics resulted to be unchanged. The second set of flights, in autumn, were done in a shorter period, during the days 16-17-18 October 2013, thus obtaining even better homogeneity of the vegetation conditions. Image and data processing are based on standard classification techniques, both pixel and object based, applied simultaneously on multispectral and multitemporal data, with the aim of producing a thematic map of the species of interest. The classification accuracies will be computed on the basis of ground truth comparison, to study possible misclassification among species.

  15. Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

    NASA Astrophysics Data System (ADS)

    Ragheb, Amr M.; Osman, Heba; Abbas, Alaa M.; Elkaffas, Saleh M.; El-Tobely, Tarek A.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; Abd El-Samie, Fathi E.

    2012-12-01

    To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity-hue-saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.

  16. Imaging of human skin lesions with the multispectral dermoscope

    NASA Astrophysics Data System (ADS)

    Kapsokalyvas, Dimitrios; Bruscino, Nicola; Alfieri, Domenico; de Giorgi, Vincenzo; Cannarozzo, Giovanni; Lotti, Torello; Pavone, Francesco S.

    2010-02-01

    The Dermoscope is a widespread and essential tool for dermatology. It reveals morphologic characteristics and aid the identification and diagnosis of a skin lesion. The diagnosis though is not always unambiguous and still the use of Dermoscopy doesn't obviate the need for histopathological verification. We report on the development of the Multispectral Dermoscope which employs high luminance LEDs with emission at three distinct spectral regions (470 nm, 530 nm, 625 nm). The illumination is polarized and an analyzer is used for the detection. Subsequent image analysis for enhancing the contrast for single scattered photons, hemoglobin absorption and melanin absorption is performed. Features like the surface texture of the skin, scattering structures in the epidermis, blood vessel morphology even in pigmented lesions and melanin localization have been observed in various skin pathologies. The device has been tested on volunteers and the features revealed have proven to be helpful in the diagnosis of skin lesions. Use of the Multispectral Dermoscope could improve the sensitivity and specificity of Dermoscopy.

  17. Landsat sattelite multi-spectral image classification of land cover and land use changes for GIS-based urbanization analysis in irrigation districts of lower Rio Grande Valley of Texas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Lower Rio Grande Valley in the south of Texas is experiencing rapid increase of population to bring up urban growth that continues influencing on the irrigation districts in the region. This study evaluated the Landsat satellite multi-spectral imagery to provide information for GIS-based urbaniz...

  18. Building Keypoint Mappings on Multispectral Images by a Cascade of Classifiers with a Resurrection Mechanism

    PubMed Central

    Li, Yong; Jing, Jing; Jin, Hongbin

    2015-01-01

    Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect. The unclassified keypoint mappings will be passed on to subsequent steps for determining whether they are correct. Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step. Observing this, we design a resurrection mechanism, so that they will be reconsidered and evaluated by the rules utilized in subsequent steps. Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods. PMID:26007729

  19. Advances in Hyperspectral and Multispectral Image Fusion and Spectral Unmixing

    NASA Astrophysics Data System (ADS)

    Lanaras, C.; Baltsavias, E.; Schindler, K.

    2015-08-01

    In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level w.r.t. the pixel size of the hyperspectral image. We assume that the two images are radiometrically corrected and geometrically co-registered. The scientific contributions of this work are (a) a simultaneous approach to image fusion and hyperspectral unmixing, (b) enforcing several physically plausible constraints during unmixing that are all well-known, but typically not used in combination, and (c) the use of efficient, state-of-the-art mathematical optimization tools to implement the processing. The results of our joint fusion and unmixing has the potential to enable more accurate and detailed semantic interpretation of objects and their properties in hyperspectral and multispectral images, with applications in environmental mapping, monitoring and change detection. In our experiments, the proposed method always improves the fusion compared to competing methods, reducing RMSE between 4% and 53%.

  20. Multispectral photoacoustic imaging of nerves with a clinical ultrasound system

    NASA Astrophysics Data System (ADS)

    Mari, Jean Martial; West, Simeon; Beard, Paul C.; Desjardins, Adrien E.

    2014-03-01

    Accurate and efficient identification of nerves is of great importance during many ultrasound-guided clinical procedures, including nerve blocks and prostate biopsies. It can be challenging to visualise nerves with conventional ultrasound imaging, however. One of the challenges is that nerves can have very similar appearances to nearby structures such as tendons. Several recent studies have highlighted the potential of near-infrared optical spectroscopy for differentiating nerves and adjacent tissues, as this modality can be sensitive to optical absorption of lipids that are present in intra- and extra-neural adipose tissue and in the myelin sheaths. These studies were limited to point measurements, however. In this pilot study, a custom photoacoustic system with a clinical ultrasound imaging probe was used to acquire multi-spectral photoacoustic images of nerves and tendons from swine ex vivo, across the wavelength range of 1100 to 1300 nm. Photoacoustic images were processed and overlaid in colour onto co-registered conventional ultrasound images that were acquired with the same imaging probe. A pronounced optical absorption peak centred at 1210 nm was observed in the photoacoustic signals obtained from nerves, and it was absent in those obtained from tendons. This absorption peak, which is consistent with the presence of lipids, provides a novel image contrast mechanism to significantly enhance the visualization of nerves. In particular, image contrast for nerves was up to 5.5 times greater with photoacoustic imaging (0.82 +/- 0.15) than with conventional ultrasound imaging (0.148 +/- 0.002), with a maximum contrast of 0.95 +/- 0.02 obtained in photoacoustic mode. This pilot study demonstrates the potential of photoacoustic imaging to improve clinical outcomes in ultrasound-guided interventions in regional anaesthesia and interventional oncology.

  1. Biological tissue identification using a multispectral imaging system

    NASA Astrophysics Data System (ADS)

    Delporte, Céline; Sautrot, Sylvie; Ben Chouikha, Mohamed; Viénot, Françoise; Alquié, Georges

    2013-02-01

    A multispectral imaging system enabling biological tissue identifying and differentiation is presented. The measurement of β(λ) spectral radiance factor cube for four tissue types (beef muscle, pork muscle, turkey muscle and beef liver) present in the same scene was carried out. Three methods for tissue identification are proposed and their relevance evaluated. The first method correlates the scene spectral radiance factor with tissue database characteristics. This method gives detection rates ranging from 63.5 % to 85 %. The second method correlates the scene spectral radiance factor derivatives with a database of tissue β(λ) derivatives. This method is more efficient than the first one because it gives detection rates ranging from 79 % to 89 % with over-detection rates smaller than 0.2 %. The third method uses the biological tissue spectral signature. It enhances contrast in order to afford tissue differentiation and identification.

  2. Spatial clustering of pixels of a multispectral image

    SciTech Connect

    Conger, James Lynn

    2014-08-19

    A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

  3. Multispectral imaging fluorescence microscopy for lymphoid tissue analysis

    NASA Astrophysics Data System (ADS)

    Monici, Monica; Agati, Giovanni; Fusi, Franco; Mazzinghi, Piero; Romano, Salvatore; Pratesi, Riccardo; Alterini, Renato; Bernabei, Pietro A.; Rigacci, Luigi

    1999-01-01

    Multispectral imaging autofluorescence microscopy (MIAM) is used here for the analysis of lymphatic tissues. Lymph node biopsies, from patients with lympthoadenopathy of different origin have been examined. Natural fluorescence (NF) images of 3 micrometers sections were obtained using three filters peaked at 450, 550 and 680 nm with 50 nm bandpass. Monochrome images were combined together in a single RGB image. NF images of lymph node tissue sections show intense blue-green fluorescence of the connective stroma. Normal tissue shows follicles with faintly fluorescent lymphocytes, as expected fro the morphologic and functional characteristics of these cells. Other more fluorescent cells (e.g., plasma cells and macrophages) are evidenced. Intense green fluorescence if localized in the inner wall of the vessels. Tissues coming from patients affected by Hodgkin's lymphoma show spread fluorescence due to connective infiltration and no evidence of follicle organization. Brightly fluorescent large cells, presumably Hodgkin cells, are also observed. These results indicate that MIAM can discriminate between normal and pathological tissues on the basis of their natural fluorescence pattern, and, therefore, represent a potentially useful technique for diagnostic applications. Analysis of the fluorescence spectra of both normal and malignant lymphoid tissues resulted much less discriminatory than MIAM.

  4. Whole-body multispectral photoacoustic imaging of adult zebrafish

    PubMed Central

    Huang, Na; Guo, Heng; Qi, Weizhi; Zhang, Zhiwei; Rong, Jian; Yuan, Zhen; Ge, Wei; Jiang, Huabei; Xi, Lei

    2016-01-01

    The zebrafish, an ideal vertebrate for studying developmental biology and genetics, is increasingly being used to understand human diseases, due to its high similarity to the human genome and its optical transparency during embryonic stages. Once the zebrafish has fully developed, especially wild-type breeds, conventional optical imaging techniques have difficulty in imaging the internal organs and structures with sufficient resolution and penetration depth. Even with established mutant lines that remain transparent throughout their life cycle, it is still challenging for purely optical imaging modalities to visualize the organs of juvenile and adult zebrafish at a micro-scale spatial resolution. In this work, we developed a non-invasive three-dimensional photoacoustic imaging platform with an optimized illumination pattern and a cylindrical-scanning-based data collection system to image entire zebrafish with micro-scale resolutions of 80 μm and 600 μm in the lateral and axial directions, respectively. In addition, we employed a multispectral strategy that utilized excitation wavelengths from 690 nm to 930 nm to statistically quantify the relative optical absorption spectrum of major organs. PMID:27699119

  5. Whole-body multispectral photoacoustic imaging of adult zebrafish

    PubMed Central

    Huang, Na; Guo, Heng; Qi, Weizhi; Zhang, Zhiwei; Rong, Jian; Yuan, Zhen; Ge, Wei; Jiang, Huabei; Xi, Lei

    2016-01-01

    The zebrafish, an ideal vertebrate for studying developmental biology and genetics, is increasingly being used to understand human diseases, due to its high similarity to the human genome and its optical transparency during embryonic stages. Once the zebrafish has fully developed, especially wild-type breeds, conventional optical imaging techniques have difficulty in imaging the internal organs and structures with sufficient resolution and penetration depth. Even with established mutant lines that remain transparent throughout their life cycle, it is still challenging for purely optical imaging modalities to visualize the organs of juvenile and adult zebrafish at a micro-scale spatial resolution. In this work, we developed a non-invasive three-dimensional photoacoustic imaging platform with an optimized illumination pattern and a cylindrical-scanning-based data collection system to image entire zebrafish with micro-scale resolutions of 80 μm and 600 μm in the lateral and axial directions, respectively. In addition, we employed a multispectral strategy that utilized excitation wavelengths from 690 nm to 930 nm to statistically quantify the relative optical absorption spectrum of major organs.

  6. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

    NASA Astrophysics Data System (ADS)

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J.; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm's burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm's accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  7. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

    PubMed

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J; Lu, Yang; Sellke, Eric W; Fan, Wensheng; DiMaio, J Michael; Thatcher, Jeffrey E

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z -test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  8. Analysis of blood and bone marrow smears using multispectral imaging analysis techniques

    NASA Astrophysics Data System (ADS)

    Wu, Qiongshui; Zeng, Libo; Ke, Hengyu; Xie, Wenjuan; Zheng, Hong; Zhang, Yan

    2005-04-01

    Counting of different classes of white blood cells in bone marrow smears can give pathologists valuable information regarding various cancers. But it is tedious to manually locate, identify, and count these classes of cells, even by skilled hands. This paper presents a novel approach for automatic detection of White Blood Cells in bone marrow microscopic images. Different from traditional color imaging method, we use multispectral imaging techniques for image acquisition. The combination of conventional digital imaging with spectroscopy can provide us with additional useful spectral information in common pathological samples. With our spectral calibration method, device-independent images can be acquired, which is almost impossible in conventional color imaging method. A novel segmentation algorithm using spectral operation is presented in this paper. Experiments show that the segmentation is robust, precise, with low computational cost and insensitive to smear staining and illumination condition. Once the nuclei and cytoplasm have been segmented, more than a hundred of features are extracted under the direction of a pathologist, including shape features, textural features and spectral ratio features. In pattern recognition, a maximum likelihood classifier(MLC) is implemented in a hierarchical tree. The classification results are also discussed. This paper is focused on image acquisition and segmentation.

  9. Assigning Main Orientation to an EOH Descriptor on Multispectral Images

    PubMed Central

    Li, Yong; Shi, Xiang; Wei, Lijun; Zou, Junwei; Chen, Fang

    2015-01-01

    This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment. PMID:26140348

  10. Multispectral fundus imaging for early detection of diabetic retinopathy

    NASA Astrophysics Data System (ADS)

    Beach, James M.; Tiedeman, James S.; Hopkins, Mark F.; Sabharwal, Yashvinder S.

    1999-04-01

    Functional imaging of the retina and associated structures may provide information for early assessment of risks of developing retinopathy in diabetic patients. Here we show results of retinal oximetry performed using multi-spectral reflectance imaging techniques to assess hemoglobin (Hb) oxygen saturation (OS) in blood vessels of the inner retina and oxygen utilization at the optic nerve in diabetic patients without retinopathy and early disease during experimental hyperglycemia. Retinal images were obtained through a fundus camera and simultaneously recorded at up to four wavelengths using image-splitting modules coupled to a digital camera. Changes in OS in large retinal vessels, in average OS in disk tissue, and in the reduced state of cytochrome oxidase (CO) at the disk were determined from changes in reflectance associated with the oxidation/reduction states of Hb and CO. Step to high sugar lowered venous oxygen saturation to a degree dependent on disease duration. Moderate increase in sugar produced higher levels of reduced CO in both the disk and surrounding tissue without a detectable change in average tissue OS. Results suggest that regulation of retinal blood supply and oxygen consumption are altered by hyperglycemia and that such functional changes are present before clinical signs of retinopathy.

  11. Radiometric Characterization of Hyperspectral Imagers using Multispectral Sensors

    NASA Technical Reports Server (NTRS)

    McCorkel, Joel; Kurt, Thome; Leisso, Nathan; Anderson, Nikolaus; Czapla-Myers, Jeff

    2009-01-01

    The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these test sites are not always successful due to weather and funding availability. Therefore, RSG has also automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor, This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral a imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (M0DIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of M0DlS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most brands as well as similar agreement between results that employ the different MODIS sensors as a reference.

  12. High Throughput Multispectral Image Processing with Applications in Food Science.

    PubMed

    Tsakanikas, Panagiotis; Pavlidis, Dimitris; Nychas, George-John

    2015-01-01

    Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing's outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples. PMID:26466349

  13. High Throughput Multispectral Image Processing with Applications in Food Science.

    PubMed

    Tsakanikas, Panagiotis; Pavlidis, Dimitris; Nychas, George-John

    2015-01-01

    Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing's outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.

  14. High Throughput Multispectral Image Processing with Applications in Food Science

    PubMed Central

    Tsakanikas, Panagiotis; Pavlidis, Dimitris; Nychas, George-John

    2015-01-01

    Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing’s outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples. PMID:26466349

  15. Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

    PubMed

    Li, Yong; Shi, Xiang; Wei, Lijun; Zou, Junwei; Chen, Fang

    2015-07-01

    This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

  16. Establish keypoint matches on multispectral images utilizing descriptor and global information over entire image

    NASA Astrophysics Data System (ADS)

    Li, Yong; Zou, Junwei; Jing, Jing; Jin, Hongbin; Yu, Hang

    2016-05-01

    This paper proposes an approach to registering multispectral images by establishing keypoint matches. The matching ability of descriptors is characterized by the repeatability and distinctiveness that typically decrease on multispectral images. The decrease of matching ability often yields a set of keypoint matches containing a high rate of incorrect matches, and in this case the outlier matches are very difficult to be removed. To establish reliable keypoint matches, this paper proposes an approach of two stages. Firstly, keypoint matches of smaller descriptor distance are obtained as an initial set. Secondly, complementary information to the local window for computing descriptors is employed to evaluate keypoint matches and find good matches. A smaller descriptor distance for a keypoint match implies a greater probability of being correct and hence the initial set contains a higher rate of correct matches. The global information can be viewed as a means of enhancing the matching ability of descriptors, compensating the decrease of common information between multispectral images. Experimental results show that the proposed method can effectively establish keypoint matches on multispectral images of large spectral difference.

  17. Imaging Science Panel. Multispectral Imaging Science Working Group joint meeting with Information Science Panel: Introduction

    NASA Technical Reports Server (NTRS)

    1982-01-01

    The state-of-the-art of multispectral sensing is reviewed and recommendations for future research and development are proposed. specifically, two generic sensor concepts were discussed. One is the multispectral pushbroom sensor utilizing linear array technology which operates in six spectral bands including two in the SWIR region and incorporates capabilities for stereo and crosstrack pointing. The second concept is the imaging spectrometer (IS) which incorporates a dispersive element and area arrays to provide both spectral and spatial information simultaneously. Other key technology areas included very large scale integration and the computer aided design of these devices.

  18. Edge-based correlation image registration for multispectral imaging

    DOEpatents

    Nandy, Prabal

    2009-11-17

    Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

  19. A new method of multispectral image processing with camouflage effect detection

    NASA Astrophysics Data System (ADS)

    Hu, Jianghua; Cui, Guangzhen; Qin, Lei

    2015-10-01

    In order to enhance the observability of multispectral image and improve the accuracy of camouflage effect evaluation based on multispectral photographic. A new method for multispectral image processing has been put forward. In the visible band, more spectral images for image fusion which are based on wavelet transformation respectively are chosen. The image information is enhanced. The visible light and near infrared band images are fused and introduced in three-channel of red, green and blue. The true color image is synthetized. While the detail of visible light image is enhanced, near infrared image information which is more interesting in camouflage evaluation is kept. Finally the fusion image are processed through histogram stretching and correlation method. The image color and luminance difference of each part is enhanced. The target recognition and camouflage effect evaluation is more advantageous. The experimental results proved that the method has a good effect.

  20. Multispectral fluorescence imaging techniques for nondestructive food safety inspection

    NASA Astrophysics Data System (ADS)

    Kim, Moon S.; Lefcourt, Alan M.; Chen, Yud-Ren

    2004-03-01

    The use of spectral sensing has gained acceptance as a rapid means for nondestructive inspection of postharvest food produce. Current technologies generally use color or a single wavelength camera technology. The applicability and sensitivity of these techniques can be expanded through the use of multiple wavelengths. Reflectance in the Vis/NIR is the prevalent spectral technique. Fluorescence, compared to reflectance, is regarded as a more sensitive technique due to its dynamic responses to subtle changes in biological entities. Our laboratory has been exploring fluorescence as a potential means for detection of quality and wholesomeness of food products. Applications of fluorescence sensing require an understanding of the spectral characteristics emanating from constituents and potential contaminants. A number of factors affecting fluorescence emission characteristics are discussed. Because of relatively low fluorescence quantum yield from biological samples, a system with a powerful pulse light source such as a laser coupled with a gated detection device is used to harvest fluorescence, in the presence of ambient light. Several fluorescence sensor platforms developed in our laboratory, including hyperspectral imaging, and laser-induced fluorescence (LIF) and steady-state fluorescence imaging systems with multispectral capabilities are presented. We demonstrate the potential uses of recently developed fluorescence imaging platforms in food safety inspection of apples contaminated with animal feces.

  1. Active multispectral imaging system for photodiagnosis and personalized phototherapies

    NASA Astrophysics Data System (ADS)

    Ugarte, M. F.; Chávarri, L.; Briz, S.; Padrón, V. M.; García-Cuesta, E.

    2014-10-01

    The proposed system has been designed to identify dermatopathologies or to apply personalized phototherapy treatments. The system emits electromagnetic waves in different spectral bands in the range of visible and near infrared to irradiate the target (skin or any other object) to be spectrally characterized. Then, an imaging sensor measures the target response to the stimulus at each spectral band and, after processing, the system displays in real time two images. In one of them the value of each pixel corresponds to the more reflected wavenumber whereas in the other image the pixel value represents the energy absorbed at each band. The diagnosis capability of this system lies in its multispectral design, and the phototherapy treatments are adapted to the patient and his lesion by measuring his absorption capability. This "in situ" absorption measurement allows us to determine the more appropriate duration of the treatment according to the wavelength and recommended dose. The main advantages of this system are its low cost, it does not have moving parts or complex mechanisms, it works in real time, and it is easy to handle. For these reasons its widespread use in dermatologist consultation would facilitate the work of the dermatologist and would improve the efficiency of diagnosis and treatment. In fact the prototype has already been successfully applied to pathologies such as carcinomas, melanomas, keratosis, and nevi.

  2. Active multispectral imaging system for photodiagnosis and personalized phototherapies

    SciTech Connect

    Ugarte, M. F. E-mail: sbriz@fis.uc3m.es; Chávarri, L.; Padrón, V. M.; García-Cuesta, E.

    2014-10-15

    The proposed system has been designed to identify dermatopathologies or to apply personalized phototherapy treatments. The system emits electromagnetic waves in different spectral bands in the range of visible and near infrared to irradiate the target (skin or any other object) to be spectrally characterized. Then, an imaging sensor measures the target response to the stimulus at each spectral band and, after processing, the system displays in real time two images. In one of them the value of each pixel corresponds to the more reflected wavenumber whereas in the other image the pixel value represents the energy absorbed at each band. The diagnosis capability of this system lies in its multispectral design, and the phototherapy treatments are adapted to the patient and his lesion by measuring his absorption capability. This “in situ” absorption measurement allows us to determine the more appropriate duration of the treatment according to the wavelength and recommended dose. The main advantages of this system are its low cost, it does not have moving parts or complex mechanisms, it works in real time, and it is easy to handle. For these reasons its widespread use in dermatologist consultation would facilitate the work of the dermatologist and would improve the efficiency of diagnosis and treatment. In fact the prototype has already been successfully applied to pathologies such as carcinomas, melanomas, keratosis, and nevi.

  3. In vivo imaging of cancer cells with electroporation of quantum dots and multispectral imaging

    NASA Astrophysics Data System (ADS)

    Yoo, Jung Sun; Won, Nayoun; Kim, Hong Bae; Bang, Jiwon; Kim, Sungjee; Ahn, Saeyoung; Soh, Kwang-Sup

    2010-06-01

    Our understanding of dissemination and growth of cancer cells is limited by our inability for long-term followup of this process in vivo. Fluorescence molecular imaging has the potential to track cancer cells with high contrast and sensitivity in living animals. For this purpose, intracellular delivery of near-infrared fluorescence quantum dots (QDs) by electroporation offers considerable advantages over organic fluorophores and other cell tagging methods. In this research we developed a multispectral imaging system that could eliminate two major parameters compromising in vivo fluorescence imaging performance, i.e., variations in the tissue optical properties and tissue autofluorescence. We demonstrated that electroporation of QDs and multispectral imaging allowed in vivo assessment of cancer development and progression in the xenograft mouse tumor model for more than 1 month, providing a powerful means to learn more about the biology of cancer and metastasis.

  4. Image quality degradation and retrieval errors introduced by registration and interpolation of multispectral digital images

    SciTech Connect

    Henderson, B.G.; Borel, C.C.; Theiler, J.P.; Smith, B.W.

    1996-04-01

    Full utilization of multispectral data acquired by whiskbroom and pushbroom imagers requires that the individual channels be registered accurately. Poor registration introduces errors which can be significant, especially in high contrast areas such as boundaries between regions. We simulate the acquisition of multispectral imagery in order to estimate the errors that are introduced by co-registration of different channels and interpolation within the images. We compute the Modulation Transfer Function (MTF) and image quality degradation brought about by fractional pixel shifting and calculate errors in retrieved quantities (surface temperature and water vapor) that occur as a result of interpolation. We also present a method which might be used to estimate sensor platform motion for accurate registration of images acquired by a pushbroom scanner.

  5. Multispectral mid-infrared imaging using frequency upconversion

    NASA Astrophysics Data System (ADS)

    Sanders, Nicolai; Dam, Jeppe Seidelin; Jensen, Ole Bjarlin; Tidemand-Lichtenberg, Peter; Pedersen, Christian

    2013-03-01

    It has recently been shown that it is possible to upconvert infrared images to the near infrared region with high quantum efficiency and low noise by three-wave mixing with a laser field [1]. If the mixing laser is single-frequency, the upconverted image is simply a band-pass filtered version of the infrared object field, with a bandwidth corresponding given by the acceptance parameter of the conversion process, and a center frequency given by the phase-match condition. Tuning of the phase-matched wavelengths has previously been demonstrated by changing the temperature [2] or angle [3 Keywords: Infrared imaging, nonlinear frequency conversion, diode lasers, upconversion ] of the nonlinear material. Unfortunately, temperature tuning is slow, and angle tuning typically results in alignment issues. Here we present a novel approach where the wavelength of the mixing field is used as a tuning parameter, allowing for fast tuning and hence potentially fast image acquisition, paving the way for upconversion based real time multispectral imaging. In the present realization the upconversion module consists of an external cavity tapered diode laser in a Littrow configuration with a computer controlled feedback grating. The output from a tunable laser is used as seed for a fiber amplifier system, boosting the power to approx. 3 W over the tuning range from 1025 to 1085 nm. Using a periodically poled lithium niobate crystal, the infrared wavelength that can be phase-matched is tunable over more than 200 nm. Using a crystal with multiple poling periods allows for upconversion within the entire transparency range of the nonlinear material.

  6. Detecting early stage pressure ulcer on dark skin using multispectral imager

    NASA Astrophysics Data System (ADS)

    Yi, Dingrong; Kong, Linghua; Sprigle, Stephen; Wang, Fengtao; Wang, Chao; Liu, Fuhan; Adibi, Ali; Tummala, Rao

    2010-02-01

    We are developing a handheld multispectral imaging device to non-invasively inspect stage I pressure ulcers in dark pigmented skins without the need of touching the patient's skin. This paper reports some preliminary test results of using a proof-of-concept prototype. It also talks about the innovation's impact to traditional multispectral imaging technologies and the fields that will potentially benefit from it.

  7. Critical dimension measurement of transparent film layers by multispectral imaging.

    PubMed

    Kwon, Soonyang; Kim, Namyoon; Jo, Taeyong; Pahk, Heui Jae

    2014-07-14

    An optical microscopy system as a non-destructive method for measuring critical dimension (CD) is widely used for its stability and fastness. In case of transparent thin film measurement, it is hard to recognize the pattern under white light illumination due to its transparency and reflectance characteristics. In this paper, the optical measurement system using multispectral imaging for CD measurement of transparent thin film is introduced. The measurement system utilizes an Acousto-Optic Tunable Filter (AOTF) to illuminate the specimen with various monochromatic lights. The relationship between spectral reflectance and CD measurement are deduced from series of measurement experiments with two kinds of Indium Tin Oxide (ITO) patterned samples. When the difference of spectral reflectance between substrate and thin film layers is large enough to yield a large image intensity difference, the thin film layer can be distinguished from substrate, and it is possible to measure the CD of transparent thin films. This paper analyzes CD measurement of transparent thin film with reflectance theory and shows that the CD measurement of transparent thin film can be performed successfully with the proposed system within a certain wavelength range filtered by AOTF. PMID:25090550

  8. Infrared optical coatings for the EarthCARE Multispectral Imager.

    PubMed

    Hawkins, Gary; Woods, David; Sherwood, Richard; Djotni, Karim

    2014-10-20

    The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) Multispectral Imager (MSI) is a radiometric instrument designed to provide the imaging of the atmospheric cloud cover and the cloud top surface temperature from a sun-synchronous low Earth orbit. The MSI forms part of a suite of four instruments destined to support the European Space Agency Living Planet mission on-board the EarthCARE satellite payload to be launched in 2016, whose synergy will be used to construct three-dimensional scenes, textures, and temperatures of atmospheric clouds and aerosols. The MSI instrument contains seven channels: four solar channels to measure visible and short-wave infrared wavelengths, and three channels to measure infrared thermal emission. In this paper, we describe the optical layout of the infrared instrument channels, thin-film multilayer designs, the coating deposition method, and the spectral system throughput for the bandpass interference filters, dichroic beam splitters, lenses, and mirror coatings to discriminate wavelengths at 8.8, 10.8, and 12.0 μm. The rationale for the selection of thin-film materials, spectral measurement technique, and environmental testing performance are also presented.

  9. Sharpening advanced land imager multispectral data using a sensor model

    USGS Publications Warehouse

    Lemeshewsky, G.P.; ,

    2005-01-01

    The Advanced Land Imager (ALI) instrument on NASA's Earth Observing One (EO-1) satellite provides for nine spectral bands at 30m ground sample distance (GSD) and a 10m GSD panchromatic band. This report describes an image sharpening technique where the higher spatial resolution information of the panchromatic band is used to increase the spatial resolution of ALI multispectral (MS) data. To preserve the spectral characteristics, this technique combines reported deconvolution deblurring methods for the MS data with highpass filter-based fusion methods for the Pan data. The deblurring process uses the point spread function (PSF) model of the ALI sensor. Information includes calculation of the PSF from pre-launch calibration data. Performance was evaluated using simulated ALI MS data generated by degrading the spatial resolution of high resolution IKONOS satellite MS data. A quantitative measure of performance was the error between sharpened MS data and high resolution reference. This report also compares performance with that of a reported method that includes PSF information. Preliminary results indicate improved sharpening with the method reported here.

  10. Spatial Resolution Characterization for AWiFS Multispectral Images

    NASA Technical Reports Server (NTRS)

    Blonski, Slawomir; Ryan, Robert E.; Pagnutti, Mary; Stanley, Thomas

    2006-01-01

    Within the framework of the Joint Agency Commercial Imagery Evaluation program, the National Aeronautics and Space Administration, the National Geospatial-Intelligence Agency, and the U.S. Geological Survey cooperate in the characterization of high-to-moderate-resolution commercial imagery of mutual interest. One of the systems involved in this effort is the Advanced Wide Field Sensor (AWiFS) onboard the Indian Remote Sensing (IRS) Reourcesat-1 satellite, IRS-P6. Spatial resolution of the AWiFS multispectral images was characterized by estimating the value of the system Modulation Transfer Function (MTF) at the Nyquist spatial frequency. The Nyquist frequency is defined as half the sampling frequency, and the sampling frequency is equal to the inverse of the ground sample distance. The MTF was calculated as a ratio of the Fourier transform of a profile across an AWiFS image of the Lake Pontchartrain Causeway Bridge and the Fourier transform of a profile across an idealized model of the bridge for each spectral band evaluated. The mean MTF value for the AWiFS imagery evaluated was estimated to be 0.1.

  11. Infrared optical coatings for the EarthCARE Multispectral Imager.

    PubMed

    Hawkins, Gary; Woods, David; Sherwood, Richard; Djotni, Karim

    2014-10-20

    The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) Multispectral Imager (MSI) is a radiometric instrument designed to provide the imaging of the atmospheric cloud cover and the cloud top surface temperature from a sun-synchronous low Earth orbit. The MSI forms part of a suite of four instruments destined to support the European Space Agency Living Planet mission on-board the EarthCARE satellite payload to be launched in 2016, whose synergy will be used to construct three-dimensional scenes, textures, and temperatures of atmospheric clouds and aerosols. The MSI instrument contains seven channels: four solar channels to measure visible and short-wave infrared wavelengths, and three channels to measure infrared thermal emission. In this paper, we describe the optical layout of the infrared instrument channels, thin-film multilayer designs, the coating deposition method, and the spectral system throughput for the bandpass interference filters, dichroic beam splitters, lenses, and mirror coatings to discriminate wavelengths at 8.8, 10.8, and 12.0 μm. The rationale for the selection of thin-film materials, spectral measurement technique, and environmental testing performance are also presented. PMID:25402784

  12. Multispectral Image Road Extraction Based Upon Automated Map Conflation

    NASA Astrophysics Data System (ADS)

    Chen, Bin

    Road network extraction from remotely sensed imagery enables many important and diverse applications such as vehicle tracking, drone navigation, and intelligent transportation studies. There are, however, a number of challenges to road detection from an image. Road pavement material, width, direction, and topology vary across a scene. Complete or partial occlusions caused by nearby buildings, trees, and the shadows cast by them, make maintaining road connectivity difficult. The problems posed by occlusions are exacerbated with the increasing use of oblique imagery from aerial and satellite platforms. Further, common objects such as rooftops and parking lots are made of materials similar or identical to road pavements. This problem of common materials is a classic case of a single land cover material existing for different land use scenarios. This work addresses these problems in road extraction from geo-referenced imagery by leveraging the OpenStreetMap digital road map to guide image-based road extraction. The crowd-sourced cartography has the advantages of worldwide coverage that is constantly updated. The derived road vectors follow only roads and so can serve to guide image-based road extraction with minimal confusion from occlusions and changes in road material. On the other hand, the vector road map has no information on road widths and misalignments between the vector map and the geo-referenced image are small but nonsystematic. Properly correcting misalignment between two geospatial datasets, also known as map conflation, is an essential step. A generic framework requiring minimal human intervention is described for multispectral image road extraction and automatic road map conflation. The approach relies on the road feature generation of a binary mask and a corresponding curvilinear image. A method for generating the binary road mask from the image by applying a spectral measure is presented. The spectral measure, called anisotropy-tunable distance (ATD

  13. A channel-based color fusion technique using multispectral images for night vision enhancement

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2011-09-01

    A fused image using multispectral images can increase the reliability of interpretation because it combines the complimentary information apparent in multispectral images. While a color image can be easily interpreted by human users (for visual analysis), and thus improves observer performance and reaction times. We propose a fast color fusion method, termed as channel-based color fusion, which is efficient for real time applications. Notice that the term of "color fusion" means combing multispectral images into a color-version image with the purpose of resembling natural scenes. On the other hand, false coloring technique usually has no intention of resembling natural scenery. The framework of channel-based color fusion is as follows, (1) prepare for color fusion by preprocessing, image registration and fusion; (2) form a color fusion image by properly assigning multispectral images to red, green, and blue channels; (3) fuse multispectral images (gray fusion) using a wavelet-based fusion algorithm; and (4) replace the value component of color fusion in HSV color space with the gray-fusion image, and finally transform back to RGB space. In night vision imaging, there may be two or several bands of images available, for example, visible (RGB), image intensified (II), near infrared (NIR), medium wave infrared (MWIR), long wave infrared (LWIR). The proposed channel-wise color fusions were tested with two-band (e.g., NIR + LWIR, II + LWIR, RGB + LWIR) or three-band (e.g., RGB + NIR + LWIR) multispectral images. Experimental results show that the colors in the fused images by the proposed method are vivid and comparable with that of the segmentation-based colorization. The processing speed of new method is much faster than any segmentation-based method.

  14. Shuttle Imaging Radar-A (SIR-A) data as a complement to Landsat Multispectral Scanner (MSS) data

    NASA Technical Reports Server (NTRS)

    Henninger, D. L.; Carney, J. H.

    1983-01-01

    Principal components analysis and supervised classifications were performed on two dates of Landsat multispectral scanner (MSS) data registered to one date of Shuttle Imaging Radar-A (SIR-A) data in a wheat-growing area of New South Wales, Australia. The purpose was to evaluate SIR-A data as a complement to Landsat MSS data in an agricultural environment. The SIR-A data was filtered using a 7 x 7 pixel moving window median filter. Principal components analysis indicated the SIR-A data were discriminating between trees and agricultural fields. Supervised classifications using wheat, pasture, trees, and idle classes resulted in increased accuracies for wheat and pasture and slightly decreased accuracies for trees and idle for the Landsat MSS/SIR-A registered data sets over the Landsat MSS alone. Overall classification accuracies were unchanged for one date and substantially increased for the other when the SIR-A data were added to the Landsat MSS data.

  15. Multispectral Imager With Improved Filter Wheel and Optics

    NASA Technical Reports Server (NTRS)

    Bremer, James C.

    2007-01-01

    Figure 1 schematically depicts an improved multispectral imaging system of the type that utilizes a filter wheel that contains multiple discrete narrow-band-pass filters and that is rotated at a constant high speed to acquire images in rapid succession in the corresponding spectral bands. The improvement, relative to prior systems of this type, consists of the measures taken to prevent the exposure of a focal-plane array (FPA) of photodetectors to light in more than one spectral band at any given time and to prevent exposure of the array to any light during readout. In prior systems, these measures have included, variously the use of mechanical shutters or the incorporation of wide opaque sectors (equivalent to mechanical shutters) into filter wheels. These measures introduce substantial dead times into each operating cycle intervals during which image information cannot be collected and thus incoming light is wasted. In contrast, the present improved design does not involve shutters or wide opaque sectors, and it reduces dead times substantially. The improved multispectral imaging system is preceded by an afocal telescope and includes a filter wheel positioned so that its rotation brings each filter, in its turn, into the exit pupil of the telescope. The filter wheel contains an even number of narrow-band-pass filters separated by narrow, spoke-like opaque sectors. The geometric width of each filter exceeds the cross-sectional width of the light beam coming out of the telescope. The light transmitted by the sequence of narrow-band filters is incident on a dichroic beam splitter that reflects in a broad shorter-wavelength spectral band that contains half of the narrow bands and transmits in a broad longer-wavelength spectral band that contains the other half of the narrow spectral bands. The filters are arranged on the wheel so that if the pass band of a given filter is in the reflection band of the dichroic beam splitter, then the pass band of the adjacent filter

  16. A method of spatial mapping and reclassification for high-spatial-resolution remote sensing image classification.

    PubMed

    Wang, Guizhou; Liu, Jianbo; He, Guojin

    2013-01-01

    This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

  17. Terahertz detectors for long wavelength multi-spectral imaging.

    SciTech Connect

    Lyo, Sungkwun Kenneth; Wanke, Michael Clement; Reno, John Louis; Shaner, Eric Arthur; Grine, Albert D.

    2007-10-01

    The purpose of this work was to develop a wavelength tunable detector for Terahertz spectroscopy and imaging. Our approach was to utilize plasmons in the channel of a specially designed field-effect transistor called the grating-gate detector. Grating-gate detectors exhibit narrow-linewidth, broad spectral tunability through application of a gate bias, and no angular dependence in their photoresponse. As such, if suitable sensitivity can be attained, they are viable candidates for Terahertz multi-spectral focal plane arrays. When this work began, grating-gate gate detectors, while having many promising characteristics, had a noise-equivalent power (NEP) of only 10{sup -5} W/{radical}Hz. Over the duration of this project, we have obtained a true NEP of 10{sup -8} W/{radical}Hz and a scaled NEP of 10{sup -9}W/{radical}Hz. The ultimate goal for these detectors is to reach a NEP in the 10{sup -9{yields}-10}W/{radical}Hz range; we have not yet seen a roadblock to continued improvement.

  18. Design Considerations, Modeling and Analysis for the Multispectral Thermal Imager

    SciTech Connect

    Borel, C.C.; Clodius, W.B.; Cooke, B.J.; Smith, B.W.; Weber, P.G.

    1999-02-01

    The design of remote sensing systems is driven by the need to provide cost-effective, substantive answers to questions posed by our customers. This is especially important for space-based systems, which tend to be expensive, and which generally cannot be changed after they are launched. We report here on the approach we employed in developing the desired attributes of a satellite mission, namely the Multispectral Thermal Imager. After an initial scoping study, we applied a procedure which we call: "End-to-end modeling and analysis (EEM)." We began with target attributes, translated to observable signatures and then propagated the signatures through the atmosphere to the sensor location. We modeled the sensor attributes to yield a simulated data stream, which was then analyzed to retrieve information about the original target. The retrieved signature was then compared to the original to obtain a figure of merit: hence the term "end-to-end modeling and analysis." We base the EEM in physics to ensure high fidelity and to permit scaling. As the actual design of the payload evolves, and as real hardware is tested, we can update the EEM to facilitate trade studies, and to judge, for example, whether components that deviate from specifications are acceptable.

  19. MESSENGER Multispectral Imaging of the Surface of Mercury

    NASA Astrophysics Data System (ADS)

    Blewett, D. T.; Robinson, M. S.; Denevi, B. W.; Prockter, L. M.; Murchie, S. L.; Gillis-Davis, J. J.; Head, J. W.; Domingue, D. L.; Izenberg, N. R.; McClintock, W. E.; Holsclaw, G. M.; Sprague, A. L.; Vilas, F.

    2008-05-01

    The January 2008 flyby of Mercury by the MESSENGER spacecraft provided the first close-up images of the planet since the Mariner 10 observations of 1974-75. MESSENGER's Mercury Dual Imaging System (MDIS) collected high signal-to-noise-ratio images with two cameras: a high-spatial-resolution broadband visible Narrow Angle Camera (NAC), and a lower-spatial- resolution multispectral Wide Angle Camera (WAC). The WAC has 11 narrow-band color filters with center wavelengths in the range 430 to 1020 nm. A spot spectrometer, the Visible and Infrared Spectrograph (VIRS) component of the Mercury Atmospheric and Surface Composition Spectrometer (MASCS), collected spectra over the range 350 to 1450 nm. The MESSENGER observations cover portions of the planet seen by Mariner 10, as well as new regions not previously imaged by spacecraft. Studies of the Moon over the past forty years — via spectral analysis of returned lunar samples, Earth-based telescopic reflectance spectra, and imaging by the Galileo and Clementine spacecraft, along with theoretical work on the interaction of light with a silicate regolith — provide a framework for interpreting spectra of Mercury. Ferrous iron in silicate minerals (primarily pyroxene and olivine) and glasses has a strong influence on lunar reflectance spectra, producing diagnostic absorption features. "Space weathering," the response of a regolith to micrometeorite bombardment and solar-wind sputtering, tends to reduce the contrast of absorption bands and introduces an overall strong positive ("red") slope to the spectrum. These spectral effects are attributed to tiny metallic iron particles created by vapor deposition during space weathering. Spectrally neutral opaque phases are dark with a flat "bluish" spectral slope and lack strong absorptions. Ilmenite, an iron-titanium oxide, is the key opaque phase in lunar samples. The MESSENGER multispectral observations validate and greatly extend the knowledge gained from analysis of Mariner 10

  20. Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification

    NASA Astrophysics Data System (ADS)

    Xiao, Yan; Jiang, Qigang; Wang, Bin; Li, Yuanhua; Liu, Shu; Cui, Can

    2016-04-01

    The contribution of the integration of optical and polarimetric synthetic aperture radar (PolSAR) data to accurate land-cover classification was investigated. For this purpose, an object-oriented classification methodology that consisted of polarimetric decomposition, hybrid feature selection, and a support vector machine (SVM) was proposed. A RADARSAT-2 Fine Quad-Pol image and an HJ-1A CCD2 multispectral image were used as data sources. First, polarimetric decomposition was implemented for the RADARSAT-2 image. Sixty-one polarimetric parameters were extracted using different polarimetric decomposition methods and then merged with the main diagonal elements (T11, T22, T33) of the coherency matrix to form a multichannel image with 64 layers. Second, the HJ-1A and the multichannel images were divided into numerous image objects by implementing multiresolution segmentation. Third, 1104 features were extracted from the HJ-1A and the multichannel images for each image object. Fourth, the hybrid feature selection method that combined the ReliefF filter approach and the genetic algorithm (GA) wrapper approach (ReliefF-GA) was used. Finally, land-cover classification was performed by an SVM classifier on the basis of the selected features. Five other classification methodologies were conducted for comparison to verify the contribution of optical and PolSAR data integration and to test the superiority of the proposed object-oriented classification methodology. Comparison results show that HJ-1A data, RADARSAT-2 data, polarimetric decomposition, ReliefF-GA, and SVM have a significant contribution by improving land-cover classification accuracy.

  1. Multispectral imaging of organ viability during uterine transplantation surgery

    NASA Astrophysics Data System (ADS)

    Clancy, Neil T.; Saso, Srdjan; Stoyanov, Danail; Sauvage, Vincent; Corless, David J.; Boyd, Michael; Noakes, David E.; Thum, Meen-Yau; Ghaem-Maghami, Sadaf; Smith, J. R.; Elson, Daniel S.

    2014-02-01

    Uterine transplantation surgery has been proposed as a treatment for permanent absolute uterine factor infertility (AUFI) in the case of loss of the uterus. Due to the complexity of the vasculature correct reanastomosis of the blood supply during transplantation surgery is a crucial step to ensure reperfusion and viability of the organ. While techniques such as fluorescent dye imaging have been proposed to visualise perfusion there is no gold standard for intraoperative visualisation of tissue oxygenation. In this paper results from a liquid crystal tuneable filter (LCTF)-based multispectral imaging (MSI) laparoscope are described. The system was used to monitor uterine oxygen saturation (SaO2) before and after transplantation. Results from surgeries on two animal models (rabbits and sheep) are presented. A feature-based registration algorithm was used to correct for misalignment induced by breathing or peristalsis in the tissues of interest prior to analysis. An absorption spectrum was calculated at each spatial pixel location using reflectance data from a reference standard, and the relative contributions from oxy- and deoxyhaemoglobin were calculated using a least squares regression algorithm with non-negativity constraints. Results acquired during animal surgeries show that cornual oxygenation changes are consistent with those observed in point measurements taken using a pulse oximeter, showing reduced SaO2 following reanastomosis. Values obtained using the MSI laparoscope were lower than those taken with the pulse oximeter, which may be due to the latter's use of the pulsatile arterial blood signal. Future work incorporating immunological test results will help to correlate SaO2 levels with surgical outcomes.

  2. Software defined multi-spectral imaging for Arctic sensor networks

    NASA Astrophysics Data System (ADS)

    Siewert, Sam; Angoth, Vivek; Krishnamurthy, Ramnarayan; Mani, Karthikeyan; Mock, Kenrick; Singh, Surjith B.; Srivistava, Saurav; Wagner, Chris; Claus, Ryan; Vis, Matthew Demi

    2016-05-01

    Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop

  3. Initial clinical testing of a multi-spectral imaging system built on a smartphone platform

    NASA Astrophysics Data System (ADS)

    Mink, Jonah W.; Wexler, Shraga; Bolton, Frank J.; Hummel, Charles; Kahn, Bruce S.; Levitz, David

    2016-03-01

    Multi-spectral imaging systems are often expensive and bulky. An innovative multi-spectral imaging system was fitted onto a mobile colposcope, an imaging system built around a smartphone in order to image the uterine cervix from outside the body. The multi-spectral mobile colposcope (MSMC) acquires images at different wavelengths. This paper presents the clinical testing of MSMC imaging (technical validation of the MSMC system is described elsewhere 1 ). Patients who were referred to colposcopy following abnormal screening test (Pap or HPV DNA test) according to the standard of care were enrolled. Multi-spectral image sets of the cervix were acquired, consisting of images from the various wavelengths. Image acquisition took 1-2 sec. Areas suspected for dysplasia under white light imaging were biopsied, according to the standard of care. Biopsied sites were recorded on a clockface map of the cervix. Following the procedure, MSMC data was processed from the sites of biopsied sites. To date, the initial histopathological results are still outstanding. Qualitatively, structures in the cervical images were sharper at lower wavelengths than higher wavelengths. Patients tolerated imaging well. The result suggests MSMC holds promise for cervical imaging.

  4. Fast Multispectral Imaging by Spatial Pixel-Binning and Spectral Unmixing.

    PubMed

    Pan, Zhi-Wei; Shen, Hui-Liang; Li, Chunguang; Chen, Shu-Jie; Xin, John H

    2016-08-01

    Multispectral imaging system is of wide application in relevant fields for its capability in acquiring spectral information of scenes. Its limitation is that, due to the large number of spectral channels, the imaging process can be quite time-consuming when capturing high-resolution (HR) multispectral images. To resolve this limitation, this paper proposes a fast multispectral imaging framework based on the image sensor pixel-binning and spectral unmixing techniques. The framework comprises a fast imaging stage and a computational reconstruction stage. In the imaging stage, only a few spectral images are acquired in HR, while most spectral images are acquired in low resolution (LR). The LR images are captured by applying pixel binning on the image sensor, such that the exposure time can be greatly reduced. In the reconstruction stage, an optimal number of basis spectra are computed and the signal-dependent noise statistics are estimated. Then the unknown HR images are efficiently reconstructed by solving a closed-form cost function that models the spatial and spectral degradations. The effectiveness of the proposed framework is evaluated using real-scene multispectral images. Experimental results validate that, in general, the method outperforms the state of the arts in terms of reconstruction accuracy, with additional 20× or more improvement in computational efficiency.

  5. Hybrid Image Fusion for Sharpness Enhancement of Multi-Spectral Lunar Images

    NASA Astrophysics Data System (ADS)

    Awumah, Anna; Mahanti, Prasun; Robinson, Mark

    2016-10-01

    Image fusion enhances the sharpness of a multi-spectral (MS) image by incorporating spatial details from a higher-resolution panchromatic (Pan) image [1,2]. Known applications of image fusion for planetary images are rare, although image fusion is well-known for its applications to Earth-based remote sensing. In a recent work [3], six different image fusion algorithms were implemented and their performances were verified with images from the Lunar Reconnaissance Orbiter (LRO) Camera. The image fusion procedure obtained a high-resolution multi-spectral (HRMS) product from the LRO Narrow Angle Camera (used as Pan) and LRO Wide Angle Camera (used as MS) images. The results showed that the Intensity-Hue-Saturation (IHS) algorithm results in a high-spatial quality product while the Wavelet-based image fusion algorithm best preserves spectral quality among all the algorithms. In this work we show the results of a hybrid IHS-Wavelet image fusion algorithm when applied to LROC MS images. The hybrid method provides the best HRMS product - both in terms of spatial resolution and preservation of spectral details. Results from hybrid image fusion can enable new science and increase the science return from existing LROC images.[1] Pohl, Cle, and John L. Van Genderen. "Review article multisensor image fusion in remote sensing: concepts, methods and applications." International journal of remote sensing 19.5 (1998): 823-854.[2] Zhang, Yun. "Understanding image fusion." Photogramm. Eng. Remote Sens 70.6 (2004): 657-661.[3] Mahanti, Prasun et al. "Enhancement of spatial resolution of the LROC Wide Angle Camera images." Archives, XXIII ISPRS Congress Archives (2016).

  6. Superresolution image reconstruction using panchromatic and multispectral image fusion

    NASA Astrophysics Data System (ADS)

    Elbakary, M. I.; Alam, M. S.

    2008-08-01

    Hyperspectral imagery is used for a wide variety of applications, including target detection, tacking, agricultural monitoring and natural resources exploration. The main reason for using hyperspectral imagery is that these images reveal spectral information about the scene that is not available in a single band. Unfortunately, many factors such as sensor noise and atmospheric scattering degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images using co-registered high special-resolution imagery such as panchromatic imagery. In this paper, we propose a new algorithm to enhance the spatial resolution of low resolution hyperspectral bands using strongly correlated and co-registered high special-resolution panchromatic imagery. The proposed algorithm constructs the superresolution bands corresponding to the low resolution bands to enhance the resolution using a global correlation enhancement technique. The global enhancement is based on the least square regression and the histogram matching to improve the estimated interpolation of the spatial resolution. The introduced algorithm is considered as an improvement for Price’s algorithm which uses the global correlation only for the spatial resolution enhancement. Numerous studies are conducted to investigate the effect of the proposed algorithm for achieving the enhancement compared to the traditional algorithm for superresolution enhancement. Experiments results obtained using hyperspectral data derived from airborne imaging sensor are presented to verify the superiority of the proposed algorithm.

  7. Color image reproduction based on multispectral and multiprimary imaging: experimental evaluation

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Masahiro; Teraji, Taishi; Ohsawa, Kenro; Uchiyama, Toshio; Motomura, Hideto; Murakami, Yuri; Ohyama, Nagaaki

    2001-12-01

    Multispectral imaging is significant technology for the acquisition and display of accurate color information. Natural color reproduction under arbitrary illumination becomes possible using spectral information of both image and illumination light. In addition, multiprimary color display, i.e., using more than three primary colors, has been also developed for the reproduction of expanded color gamut, and for discounting observer metamerism. In this paper, we present the concept for the multispectral data interchange for natural color reproduction, and the experimental results using 16-band multispectral camera and 6-primary color display. In the experiment, the accuracy of color reproduction is evaluated in CIE (Delta) Ea*b* for both image capture and display systems. The average and maximum (Delta) Ea*b* = 1.0 and 2.1 in 16-band mutispectral camera system, using Macbeth 24 color patches. In the six-primary color projection display, average and maximum (Delta) Ea*b* = 1.3 and 2.7 with 30 test colors inside the display gamut. Moreover, the color reproduction results with different spectral distributions but same CIE tristimulus value are visually compared, and it is confirmed that the 6-primary display gives improved agreement between the original and reproduced colors.

  8. MA_MISS: Mars Multispectral Imager for Subsurface Studies

    NASA Astrophysics Data System (ADS)

    De Sanctis, M. C.; Coradini, A.; Ammannito, E.; Boccaccini, A.; Di Iorio, T.; Battistelli, E.; Capanni, A.

    2012-04-01

    A Drilling system, coupled with an in situ analysis package, is installed on the ExoMars Pasteur Rover to perform in situ investigations up to 2m in the Mars soil. Ma_Miss (Mars Multispectral Imager for Subsurface Studies) is a spectrometer devoted to observe the lateral wall of the borehole generated by the Drilling system. The instrument is fully integrated with the Drill and shares its structure and electronics. For the first time in Mars exploration experiments the water/geochemical environment will be investigated as function of depth in the shallow subsurface. Samples from the subsurface of Martian soil are unaltered by weathering process, oxidation and erosion. Subsurface access can be the key to look for signs of present and past environmental conditions, associated to the possibility for life (water, volatiles and weathering process). The analysis of uncontaminated samples by means of instrumented Drill and in situ observations is the solution for unambiguous interpretation of the original environment that leading to the formation of rocks. Ma_Miss experiment is perfectly suited to perform multispectral imaging of the drilled layers. Ma_Miss is a miniaturized near-infrared imaging spectrometer in the range 0.4-2.2 µm with 20nm spectral sampling. The task of illuminating the borehole wall and collecting the diffused light from the illuminated spot on the target requires a transparent window on the Drill tool, which shall prevent the dust contamination of the optical and mechanical elements inside. Hardness of sapphire is the closest to diamond one, thus avoiding the risk of scratches on its surface. The Sapphire window is cylindrical, and bounded such as to realize a continuous auger profile. Ma_Miss Optical Head performs the double task of illuminating the borehole wall with a spot around 1 mm diameter and of collecting the scattered light coming from a 0.1 mm diameter spot of the target. The signal from the Optical Head to the spectrometer is transferred

  9. Enhancement classification of galaxy images

    NASA Astrophysics Data System (ADS)

    Jenkinson, John

    With the advent of astronomical imaging technology developments, and the increased capacity of digital storage, the production of photographic atlases of the night sky have begun to generate volumes of data which need to be processed autonomously. As part of the Tonantzintla Digital Sky Survey construction, the present work involves software development for the digital image processing of astronomical images, in particular operations that preface feature extraction and classification. Recognition of galaxies in these images is the primary objective of the present work. Many galaxy images have poor resolution or contain faint galaxy features, resulting in the misclassification of galaxies. An enhancement of these images by the method of the Heap transform is proposed, and experimental results are provided which demonstrate the image enhancement to improve the presence of faint galaxy features thereby improving classification accuracy. The feature extraction was performed using morphological features that have been widely used in previous automated galaxy investigations. Principal component analysis was applied to the original and enhanced data sets for a performance comparison between the original and reduced features spaces. Classification was performed by the Support Vector Machine learning algorithm.

  10. Corpus Callosum MR Image Classification

    NASA Astrophysics Data System (ADS)

    Elsayed, A.; Coenen, F.; Jiang, C.; García-Fiñana, M.; Sluming, V.

    An approach to classifying Magnetic Resonance (MR) image data is described. The specific application is the classification of MRI scan data according to the nature of the corpus callosum, however the approach has more general applicability. A variation of the “spectral segmentation with multi-scale graph decomposition” mechanism is introduced. The result of the segmentation is stored in a quad-tree data structure to which a weighted variation (also developed by the authors) of the gSpan algorithm is applied to identify frequent sub-trees. As a result the images are expressed as a set frequent sub-trees. There may be a great many of these and thus a decision tree based feature reduction technique is applied before classification takes place. The results show that the proposed approach performs both efficiently and effectively, obtaining a classification accuracy of over 95% in the case of the given application.

  11. Compression of multispectral fluorescence microscopic images based on a modified set partitioning in hierarchal trees

    NASA Astrophysics Data System (ADS)

    Mansoor, Awais; Robinson, J. Paul; Rajwa, Bartek

    2009-02-01

    Modern automated microscopic imaging techniques such as high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging are capable of producing hundreds to thousands of images per experiment. For quick retrieval, fast transmission, and storage economy, these images should be saved in a compressed format. A considerable number of techniques based on interband and intraband redundancies of multispectral images have been proposed in the literature for the compression of multispectral and 3D temporal data. However, these works have been carried out mostly in the elds of remote sensing and video processing. Compression for multispectral optical microscopy imaging, with its own set of specialized requirements, has remained under-investigated. Digital photography{oriented 2D compression techniques like JPEG (ISO/IEC IS 10918-1) and JPEG2000 (ISO/IEC 15444-1) are generally adopted for multispectral images which optimize visual quality but do not necessarily preserve the integrity of scientic data, not to mention the suboptimal performance of 2D compression techniques in compressing 3D images. Herein we report our work on a new low bit-rate wavelet-based compression scheme for multispectral fluorescence biological imaging. The sparsity of signicant coefficients in high-frequency subbands of multispectral microscopic images is found to be much greater than in natural images; therefore a quad-tree concept such as Said et al.'s SPIHT1 along with correlation of insignicant wavelet coefficients has been proposed to further exploit redundancy at high-frequency subbands. Our work propose a 3D extension to SPIHT, incorporating a new hierarchal inter- and intra-spectral relationship amongst the coefficients of 3D wavelet-decomposed image. The new relationship, apart from adopting the parent-child relationship of classical SPIHT, also brought forth the conditional "sibling" relationship by relating only the insignicant wavelet coefficients of subbands

  12. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES

    SciTech Connect

    S. PERKINS; N. HARVEY

    2001-02-01

    The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.

  13. Deep learning for image classification

    NASA Astrophysics Data System (ADS)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  14. Multi-temporal remote sensing image classification - a multi-view approach

    SciTech Connect

    Chandola, Varun; Vatsavai, Raju

    2010-01-01

    Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.

  15. Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This research developed a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet/blue LED excitation for detection of fecal contamination on Golden Delicious apples. Using a hyperspectral line-scan imaging system consisting of an EMCCD camera, spectrograph, an...

  16. A novel coarse-to-fine method for registration of multispectral images

    NASA Astrophysics Data System (ADS)

    Jin, Hongbin; Fan, Chunxiao; Li, Yong; Xu, Liangpeng

    2016-07-01

    Due to non-linear intensity changes between multispectral images, the existed descriptors often yield low matching performance. In order to build reliable keypoint mappings on multispectral images, a novel coarse-to-fine method is designed using projective transformation and the information of edge overlap. The method consists of a coarse process and a fine-tuning process. In the coarse process, initial keypoint mappings are built with the descriptors associated with keypoints and the relative distance constraints are employed on them to remove outliers. In the fine-tuning process, the edge overlap information is utilized as similarity metric and an iterative framework is applied to search correct keypoint mappings. The performance of the proposed is investigated with keypoints extracted by speeded-up robust features. The experiment results show that the proposed method can build more reliable keypoint mappings on multispectral images than existed methods.

  17. Optimal design of neural networks for land-cover classification from multispectral imagery

    NASA Astrophysics Data System (ADS)

    Silvan-Cardenas, Jose L.

    2004-02-01

    It has long been shown the effectiveness of artificial neural networks to solve highly non-linear problems such as land-cover classification based on multispectral imagery. However, due to the large amount of data that is processed within this kind of applications, it is desirable to design networks with the lowest number of neurons that are capable to separate all of the given classes. At present, there are several methods intended to determine this optimal network. Most of them involve adjoining or pruning hidden neurons followed by further training in iterative fashion, which is generally a very slow process. As an alternative, the approach described in this paper is based on the computation of centroids of relevant clusters for each class samples through the well known clustering method ISODATA. A proper tessellation of the ISODATA centroids allows first the determination of the minimum number of neurons in the first hidden layer that are required to effectively separate all of the classes; and secondly, to compute weight and bias parameters for such neurons. Then, the minimum network required to perform the logic function that combines the halfspaces generated by the first layer into class-discriminant surfaces is determined via a logic function reduction method. This approach is much faster than that of current methods because it allows to determine the optimum network size and compute weight and bias parameters without further iterative adjustments. The procedure was tested with landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Results indicated that (1) the network exhibits good generalization behavior and (2) classification accuracies do not depend on the class boundary complexity but only on the class overlapping extent.

  18. Color image authentication scheme via multispectral photon-counting double random phase encoding

    NASA Astrophysics Data System (ADS)

    Moon, Inkyu

    2015-05-01

    In this paper, we present an overview of a color image authentication scheme via multispectral photon-counting (MPCI) double random phase encoding (DRPE). The MPCI makes image sparse distributed and DRPE lets image be stationary white noise which make intruder attacks difficult. In this method, the original RGB image is down-sampled into Bayer image and then be encrypted with DRPE. The encrypted image is photon-counted and transmitted on internet channel. For image authentication, the decrypted Bayer image is interpolated into RBC image with demosaicing algorithm. Experimental results show that the decrypted image is not visually recognized under low light level but can be verified with nonlinear correlation algorithm.

  19. Diffuse reflectance and fluorescence multispectral imaging system for assessment of skin

    NASA Astrophysics Data System (ADS)

    Saknite, Inga; Jakovels, Dainis; Spigulis, Janis

    2014-05-01

    The diffuse reflectance multispectral imaging technique has been used for distant mapping of in vivo skin chromophores (hemoglobin and melanin). The fluorescence multispectral imaging is not so common for skin applications due to complicity of data acquisition and processing, but could provide additional information about skin fluorophores. Both techniques are compatible, and could be combined into a multimodal solution. The multispectral imaging system Nuance based on liquid crystal tunable filters was adapted for diffuse reflectance and fluorescence spectral imaging of in vivo skin. Uniform illumination was achieved by LED ring light. Combination of four LEDs (warm white, 770 nm, 830 nm and 890 nm) was used to support diffuse reflectance mode in spectral range 450-950 nm. 405 nm LEDs were used for excitation of skin autofluorescence. Multispectral imaging system was adapted for spectral working range of 450-950 nm with scanning step of 10 nm and spectral resolution of 15 nm. An average field of view was 50x35 mm in size with spatial resolution 0,05 mm (the pixel size). Due to spectrally different illumination intensity and system sensitivity, various exposure times (from 7…500 ms) were used for each image acquisition. The proposed approach was tested for different skin lesions: benign nevus, hemangioma, basalioma and halo nevus. Spectral image cubes of different skin lesions were acquired and analyzed to test its diagnostic potential.

  20. Pseudo colour visualization of fused multispectral laser scattering images for optical diagnosis of rheumatoid arthritis

    NASA Astrophysics Data System (ADS)

    Zabarylo, U.; Minet, O.

    2010-01-01

    Investigations on the application of optical procedures for the diagnosis of rheumatism using scattered light images are only at the beginning both in terms of new image-processing methods and subsequent clinical application. For semi-automatic diagnosis using laser light, the multispectral scattered light images are registered and overlapped to pseudo-coloured images, which depict diagnostically essential contents by visually highlighting pathological changes.

  1. Multi-spectral imaging with infrared sensitive organic light emitting diode.

    PubMed

    Kim, Do Young; Lai, Tzung-Han; Lee, Jae Woong; Manders, Jesse R; So, Franky

    2014-01-01

    Commercially available near-infrared (IR) imagers are fabricated by integrating expensive epitaxial grown III-V compound semiconductor sensors with Si-based readout integrated circuits (ROIC) by indium bump bonding which significantly increases the fabrication costs of these image sensors. Furthermore, these typical III-V compound semiconductors are not sensitive to the visible region and thus cannot be used for multi-spectral (visible to near-IR) sensing. Here, a low cost infrared (IR) imaging camera is demonstrated with a commercially available digital single-lens reflex (DSLR) camera and an IR sensitive organic light emitting diode (IR-OLED). With an IR-OLED, IR images at a wavelength of 1.2 µm are directly converted to visible images which are then recorded in a Si-CMOS DSLR camera. This multi-spectral imaging system is capable of capturing images at wavelengths in the near-infrared as well as visible regions. PMID:25091589

  2. Multi-spectral imaging with infrared sensitive organic light emitting diode

    NASA Astrophysics Data System (ADS)

    Kim, Do Young; Lai, Tzung-Han; Lee, Jae Woong; Manders, Jesse R.; So, Franky

    2014-08-01

    Commercially available near-infrared (IR) imagers are fabricated by integrating expensive epitaxial grown III-V compound semiconductor sensors with Si-based readout integrated circuits (ROIC) by indium bump bonding which significantly increases the fabrication costs of these image sensors. Furthermore, these typical III-V compound semiconductors are not sensitive to the visible region and thus cannot be used for multi-spectral (visible to near-IR) sensing. Here, a low cost infrared (IR) imaging camera is demonstrated with a commercially available digital single-lens reflex (DSLR) camera and an IR sensitive organic light emitting diode (IR-OLED). With an IR-OLED, IR images at a wavelength of 1.2 µm are directly converted to visible images which are then recorded in a Si-CMOS DSLR camera. This multi-spectral imaging system is capable of capturing images at wavelengths in the near-infrared as well as visible regions.

  3. Primer on Use of Multi-Spectral and Infra Red Imaging for On-Site Inspections

    SciTech Connect

    Henderson, J R

    2010-10-26

    . Finally, an appendix provides detail describing the magnitude and spatial extent of the surface shock expected from an underground nuclear explosion. If there is a seismic event or other data to suggest there has been a nuclear explosion in violation of the CTBT, an OSI may be conducted to determine whether a nuclear explosion has occurred and to gather information which may be useful in identifying the party responsible for conducting the explosion. The OSI must be conducted in the area where the event that triggered the inspection request occurred, and the inspected area must not exceed 1,000 square kilometers, or be more than 50 km on aside (CTBT Protocol, Part II, Paragraphs 2 and 3). One of the guiding principles for an inspection is that it be effective, minimally intrusive, timely, and cost-effective [Hawkins, Feb 1998]. In that context, MSIR is one of several technologies that can be used during an aircraft overflight to identify ground regions of high interest in a timely and cost-effective manner. This allows for an optimized inspection on the ground. The primary purpose for MSIR is to identify artifacts and anomalies that might be associated with a nuclear explosion, and to use the location of those artifacts and anomalies to reduce the search area that must be inspected from the ground. The MSIR measurements can have additional utility. The multi-spectral measurements of the ground can be used for terrain classification, which can aid in geological characterization of the Inspected Area. In conditions of where light smoke or haze is present, long-wave infrared imaging can provide better imaging of the ground than is possible with standard visible imagery.

  4. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

    PubMed

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics). PMID:27475574

  5. Multispectral image compression based on DSC combined with CCSDS-IDC.

    PubMed

    Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

    2014-01-01

    Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

  6. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  7. Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

    PubMed Central

    Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

    2014-01-01

    Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches. PMID:25110741

  8. Land use classification utilizing remote multispectral scanner data and computer analysis techniques

    NASA Technical Reports Server (NTRS)

    Leblanc, P. N.; Johannsen, C. J.; Yanner, J. E.

    1973-01-01

    An airborne multispectral scanner was used to collect the visible and reflective infrared data. A small subdivision near Lafayette, Indiana was selected as the test site for the urban land use study. Multispectral scanner data were collected over the subdivision on May 1, 1970 from an altitude of 915 meters. The data were collected in twelve wavelength bands from 0.40 to 1.00 micrometers by the scanner. The results indicated that computer analysis of multispectral data can be very accurate in classifying and estimating the natural and man-made materials that characterize land uses in an urban scene.

  9. Multispectral endoscopy and microscopy imaging system using a spectrally programmable light engine

    NASA Astrophysics Data System (ADS)

    MacKinnon, N.; Stange, Ulrich; Lane, Pierre M.; MacAulay, Calum E.

    2005-03-01

    We report a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile for hyperspectral, fluorescence, or principal component imaging. Most hyperspectral or multispectral imaging systems use wavelength selection devices such as acousto-optic tunable filters (AOTFs), tunable grating or prism-based monochromators, or filter wheels. While these devices can select wavelengths they cannot create arbitrary spectral profiles. This simple and economical system can be controlled at high speed (up to 5000 illumination profiles per second). Digitally controlled illumination is bit additive with image data providing high dynamic range imaging with monochrome or color imaging devices. This is especially advantageous for endoscopes employing small well CCD or CMOS sensors since the dynamic range now can extend beyond the limits of the sensor itself. In this report we show multispectral images of in vivo tissue and in vitro tissue samples using endoscopes, surgical microscopes and conventional microscopes.

  10. Multispectral integral imaging acquisition and processing using a monochrome camera and a liquid crystal tunable filter.

    PubMed

    Latorre-Carmona, Pedro; Sánchez-Ortiga, Emilio; Xiao, Xiao; Pla, Filiberto; Martínez-Corral, Manuel; Navarro, Héctor; Saavedra, Genaro; Javidi, Bahram

    2012-11-01

    This paper presents an acquisition system and a procedure to capture 3D scenes in different spectral bands. The acquisition system is formed by a monochrome camera, and a Liquid Crystal Tunable Filter (LCTF) that allows to acquire images at different spectral bands in the [480, 680]nm wavelength interval. The Synthetic Aperture Integral Imaging acquisition technique is used to obtain the elemental images for each wavelength. These elemental images are used to computationally obtain the reconstruction planes of the 3D scene at different depth planes. The 3D profile of the acquired scene is also obtained using a minimization of the variance of the contribution of the elemental images at each image pixel. Experimental results show the viability to recover the 3D multispectral information of the scene. Integration of 3D and multispectral information could have important benefits in different areas, including skin cancer detection, remote sensing and pattern recognition, among others.

  11. 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.

  12. A Multispectral Photon-Counting Double Random Phase Encoding Scheme for Image Authentication

    PubMed Central

    Yi, Faliu; Moon, Inkyu; Lee, Yeon H.

    2014-01-01

    In this paper, we propose a new method for color image-based authentication that combines multispectral photon-counting imaging (MPCI) and double random phase encoding (DRPE) schemes. The sparsely distributed information from MPCI and the stationary white noise signal from DRPE make intruder attacks difficult. In this authentication method, the original multispectral RGB color image is down-sampled into a Bayer image. The three types of color samples (red, green and blue color) in the Bayer image are encrypted with DRPE and the amplitude part of the resulting image is photon counted. The corresponding phase information that has nonzero amplitude after photon counting is then kept for decryption. Experimental results show that the retrieved images from the proposed method do not visually resemble their original counterparts. Nevertheless, the original color image can be efficiently verified with statistical nonlinear correlations. Our experimental results also show that different interpolation algorithms applied to Bayer images result in different verification effects for multispectral RGB color images. PMID:24854208

  13. Landsat multispectral sharpening using a sensor system model and panchromatic image

    USGS Publications Warehouse

    Lemeshewsky, G.P.; ,

    2003-01-01

    The thematic mapper (TM) sensor aboard Landsats 4, 5 and enhanced TM plus (ETM+) on Landsat 7 collect imagery at 30-m sample distance in six spectral bands. New with ETM+ is a 15-m panchromatic (P) band. With image sharpening techniques, this higher resolution P data, or as an alternative, the 10-m (or 5-m) P data of the SPOT satellite, can increase the spatial resolution of the multispectral (MS) data. Sharpening requires that the lower resolution MS image be coregistered and resampled to the P data before high spatial frequency information is transferred to the MS data. For visual interpretation and machine classification tasks, it is important that the sharpened data preserve the spectral characteristics of the original low resolution data. A technique was developed for sharpening (in this case, 3:1 spatial resolution enhancement) visible spectral band data, based on a model of the sensor system point spread function (PSF) in order to maintain spectral fidelity. It combines high-pass (HP) filter sharpening methods with iterative image restoration to reduce degradations caused by sensor-system-induced blurring and resembling. Also there is a spectral fidelity requirement: sharpened MS when filtered by the modeled degradations should reproduce the low resolution source MS. Quantitative evaluation of sharpening performance was made by using simulated low resolution data generated from digital color-IR aerial photography. In comparison to the HP-filter-based sharpening method, results for the technique in this paper with simulated data show improved spectral fidelity. Preliminary results with TM 30-m visible band data sharpened with simulated 10-m panchromatic data are promising but require further study.

  14. Multispectral fluorescence image algorithms for detection of frass on mature tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at five wavebands, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm...

  15. The method of multispectral image processing of phytoplankton processing for environmental control of water pollution

    NASA Astrophysics Data System (ADS)

    Petruk, Vasil; Kvaternyuk, Sergii; Yasynska, Victoria; Kozachuk, Anastasia; Kotyra, Andrzej; Romaniuk, Ryszard S.; Askarova, Nursanat

    2015-12-01

    The paper presents improvement of the method of environmental monitoring of water bodies based on bioindication by phytoplankton, which identify phytoplankton particles carried out on the basis of comparison array multispectral images using Bayesian classifier of solving function based on Mahalanobis distance. It allows to evaluate objectively complex anthropogenic and technological impacts on aquatic ecosystems.

  16. Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Most image acquisitions from UAS have been in the visible bands, while multispectral remote sensing ap...

  17. An airborne multispectral imaging system based on two consumer-grade cameras for agricultural remote sensing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One came...

  18. Use of multispectral scanner images for assessment of hydrothermal alteration in the Marysvale, Utah, mining area.

    USGS Publications Warehouse

    Podwysocki, M.H.; Segal, D.B.; Abrams, M.J.

    1983-01-01

    Airborne multispectral scanner. A color composite image was constructed using the following spectral band ratios: 1.6/2.2 mu m, 1.6/0.48 mu m, and 0.67/1.0 mu m. The color ratio composite successfully distinguished most types of altered rocks from unaltered rocks; further division of altered rocks into ferric oxide-rich and -poor types.

  19. An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding

    NASA Technical Reports Server (NTRS)

    Jaggi, S.

    1993-01-01

    A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.

  20. Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods.

    PubMed

    Liu, Changhong; Liu, Wei; Lu, Xuzhong; Chen, Wei; Yang, Jianbo; Zheng, Lei

    2014-06-15

    Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications.

  1. Multiple classifier system for remote sensing image classification: a review.

    PubMed

    Du, Peijun; Xia, Junshi; Zhang, Wei; Tan, Kun; Liu, Yi; Liu, Sicong

    2012-01-01

    Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.

  2. Multiple Classifier System for Remote Sensing Image Classification: A Review

    PubMed Central

    Du, Peijun; Xia, Junshi; Zhang, Wei; Tan, Kun; Liu, Yi; Liu, Sicong

    2012-01-01

    Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community. PMID:22666057

  3. [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].

    PubMed

    Wu, Qian; Sun, Hong; Li, Min-zan; Song, Yuan-yuan; Zhang, Yan-e

    2015-01-01

    In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray

  4. Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images.

    PubMed

    Zanetti, Massimo; Bovolo, Francesca; Bruzzone, Lorenzo

    2015-12-01

    The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.

  5. Nonparametric segmentation of multispectral MR images incorporating spatial and intensity information

    NASA Astrophysics Data System (ADS)

    Derganc, Joze; Likar, Bostjan; Pernus, Franjo

    2002-05-01

    Image segmentation is concerned with partitioning an image into non-overlapping, constituent regions, which are homogeneous with respect to certain features. In magnetic resonance imaging (MRI), the most discriminative and commonly used features are the image intensities themselves. However, due to noise, partial volume effects, natural and spurious intensity variations, intensity distributions of distinct tissues generally overlap, which makes segmentation difficult and less precise. Using multi-spectral MR images and mapping intensities into a multidimensional feature space may help in segmentation. To further facilitate segmentation, we map the intensities and second derivatives of multi-spectral images into a common multidimensional feature space. Integration of intensity and spatial information may yield complex clusters that cannot be described by Gaussian mixture models or by hyper-spherical shapes. For this reason we devise a novel segmentation method based on non-parametric valley-seeking clustering. The valleys are found by estimating feature density gradients. The proposed segmentation method, with and without spatial information, is tested on simulated and real, single- and multi-spectral, MR brain images. The segmentation results are highly consistent with the gold standard, especially when combined with a procedure for intensity non-uniformity correction, presented in MI 4684-177.

  6. Development and bench testing of a multi-spectral imaging technology built on a smartphone platform

    NASA Astrophysics Data System (ADS)

    Bolton, Frank J.; Weiser, Reuven; Kass, Alex J.; Rose, Donny; Safir, Amit; Levitz, David

    2016-03-01

    Cervical cancer screening presents a great challenge for clinicians across the developing world. In many countries, cervical cancer screening is done by visualization with the naked eye. Simple brightfield white light imaging with photo documentation has been shown to make a significant impact on cervical cancer care. Adoption of smartphone based cervical imaging devices is increasing across Africa. However, advanced imaging technologies such as multispectral imaging systems, are seldom deployed in low resource settings, where they are needed most. To address this challenge, the optical system of a smartphone-based mobile colposcopy imaging system was refined, integrating components required for low cost, portable multi-spectral imaging of the cervix. This paper describes the refinement of the mobile colposcope to enable it to acquire images of the cervix at multiple illumination wavelengths, including modeling and laboratory testing. Wavelengths were selected to enable quantifying the main absorbers in tissue (oxyand deoxy-hemoglobin, and water), as well as scattering parameters that describe the size distribution of scatterers. The necessary hardware and software modifications are reviewed. Initial testing suggests the multi-spectral mobile device holds promise for use in low-resource settings.

  7. Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images.

    PubMed

    Zanetti, Massimo; Bovolo, Francesca; Bruzzone, Lorenzo

    2015-12-01

    The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches. PMID:26336124

  8. Segmentation of multispectral bladder MR images with inhomogeneity correction for virtual cystoscopy

    NASA Astrophysics Data System (ADS)

    Li, Lihong; Liang, Zhengrong; Wang, Su; Lu, Hongyu; Wei, Xinzhou; Wagshul, Mark; Zawin, Marlene; Posniak, Erica J.; Lee, Christopher S.

    2008-03-01

    Virtual cystoscopy (VC) is a developing noninvasive, safe, and low-cost technique for bladder cancer screening. Multispectral (T I- and T II-weighted) magnetic resonance (MR) images provide a better tissue contrast between bladder wall and bladder lumen comparing with computed tomography (CT) images. The intrinsic T I and T II contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure which is often used in CT-based VC. We propose a new partial volume (PV) segmentation scheme with inhomogeneity correction to segment multispectral MR images for tumor screening by virtual cystoscopy. The proposed PV segmentation algorithm automatically estimates the bias field and segments tissue mixtures inside each voxel of MR images, thus preserving texture information. Experimental results indicate that the present scheme is promising towards mass screening by virtual cystoscopy means.

  9. Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

    NASA Technical Reports Server (NTRS)

    Manohar, Mareboyana; Tilton, James C.

    1994-01-01

    A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.

  10. Using high resolution multispectral imaging to map Pacific coral reefs in support of UNESCO's World Heritage Central Pacific project

    NASA Astrophysics Data System (ADS)

    Siciliano, Daria; Olsen, Richard C.

    2007-10-01

    Concerns over worldwide declines in marine resources have prompted the search for innovative solutions for their conservation and management, particularly for coral reef ecosystems. Rapid advances in sensor resolution, coupled with image analysis techniques tailored to the unique optical problems of marine environments have enabled the derivation of detailed benthic habitat maps of coral reef habitats from multispectral satellite imagery. Such maps delineate coral reefs' main ecological communities, and are essential for management of these resources as baseline assessments. UNESCO's World Heritage Central Pacific Project plans to afford protection through World Heritage recognition to a number of islands and atolls in the central Pacific Ocean, including the Phoenix Archipelago in the Republic of Kiribati. Most of these islands however lack natural resource maps needed for the identification of priority areas for inclusion in a marine reserve system. Our project provides assistance to UNESCO's World Heritage Centre and the Kiribati Government by developing benthic and terrestrial habitat maps of the Phoenix Islands from high-resolution multispectral imagery. The approach involves: (i) the analysis of new Quickbird multispectral imagery; and (ii) the use of MARXAN, a simulated annealing algorithm that uses a GIS interface. Analysis of satellite imagery was performed with ENVI®, and includes removal of atmospheric effects using ATCOR (a MODTRAN4 radiative transfer model); de-glinting and water column correction algorithms; and a number of unsupervised and supervised classifiers. Previously collected ground-truth data was used to train classifications. The resulting habitat maps are then used as input to MARXAN. This algorithm ultimately identifies a proportion of each habitat to be set aside for protection, and prioritizes conservation areas. The outputs of this research are being delivered to the UNESCO World Heritage Centre office and the Kiribati Government as

  11. Implementation of a multi-spectral color imaging device without color filter array

    NASA Astrophysics Data System (ADS)

    Langfelder, G.; Longoni, A. F.; Zaraga, F.

    2011-01-01

    In this work the use of the Transverse Field Detector (TFD) as a device for multispectral image acquisition is proposed. The TFD is a color imaging pixel capable of color reconstruction without color filters. Its basic working principle is based on the generation of a suitable electric field configuration inside a Silicon depleted region by means of biasing voltages applied to surface contacts. With respect to previously proposed methods for performing multispectral capture, the TFD has a unique characteristic of electrically tunable spectral responses. This feature allows capturing an image with different sets of spectral responses (RGB, R'G'B', and so on) simply by tuning the device biasing voltages in multiple captures. In this way no hardware complexity (no external filter wheels or varying sources) is added with respect to a colorimetric device. The estimation of the spectral reflectance of the area imaged by a TFD pixel is based in this work on a linear combination of six eigenfunctions. It is shown that a spectral reconstruction can be obtained either (1) using two subsequent image captures that generate six TFD spectral responses or (2) using a new asymmetric biasing scheme, which allows the implementation of five spectral responses for each TFD pixel site in a single configuration, definitely allowing one-shot multispectral imaging.

  12. Multispectral imaging of plant stress for detection of CO2 leaking from underground

    NASA Astrophysics Data System (ADS)

    Rouse, J.; Shaw, J. A.; Repasky, K. S.; Lawrence, R. L.

    2008-12-01

    Multispectral imaging of plant stress is a potentially useful method of detecting CO2 leaking from underground. During the summers of 2007 and 2008, we deployed a multispectral imager for vegetation sensing as part of an underground CO2 release experiment conducted at the Zero Emission Research and Technology (ZERT) field site near the Montana State University campus in Bozeman, Montana. The imager was mounted on a low tower and observed the vegetation in a region near an underground pipe during a multi-week CO2 release. The imager was calibrated to measure absolute reflectance, from which vegetation indices were calculated as a measure of vegetation health. The temporal evolution of these indices over the course of the experiment show that the vegetation nearest the pipe exhibited more stress than the vegetation located further from the pipe. The imager observed notably increased stress in vegetation at locations exhibiting particularly high flux of CO2 from the ground into the atmosphere. These data from the 2007 and 2008 experiments will be used to demonstrate the utility of a tower-mounted multispectral imaging system for detecting CO2 leakage from below ground with the ability to operate continuously during clear and cloudy conditions.

  13. A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

    PubMed Central

    Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

    2009-01-01

    Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

  14. [A Method to Reconstruct Surface Reflectance Spectrum from Multispectral Image Based on Canopy Radiation Transfer Model].

    PubMed

    Zhao, Yong-guang; Ma, Ling-ling; Li, Chuan-rong; Zhu, Xiao-hua; Tang, Ling-li

    2015-07-01

    Due to the lack of enough spectral bands for multi-spectral sensor, it is difficult to reconstruct surface retlectance spectrum from finite spectral information acquired by multi-spectral instrument. Here, taking into full account of the heterogeneity of pixel from remote sensing image, a method is proposed to simulate hyperspectral data from multispectral data based on canopy radiation transfer model. This method first assumes the mixed pixels contain two types of land cover, i.e., vegetation and soil. The sensitive parameters of Soil-Leaf-Canopy (SLC) model and a soil ratio factor were retrieved from multi-spectral data based on Look-Up Table (LUT) technology. Then, by combined with a soil ratio factor, all the parameters were input into the SLC model to simulate the surface reflectance spectrum from 400 to 2 400 nm. Taking Landsat Enhanced Thematic Mapper Plus (ETM+) image as reference image, the surface reflectance spectrum was simulated. The simulated reflectance spectrum revealed different feature information of different surface types. To test the performance of this method, the simulated reflectance spectrum was convolved with the Landsat ETM + spectral response curves and Moderate Resolution Imaging Spectrometer (MODIS) spectral response curves to obtain the simulated Landsat ETM+ and MODIS image. Finally, the simulated Landsat ETM+ and MODIS images were compared with the observed Landsat ETM+ and MODIS images. The results generally showed high correction coefficients (Landsat: 0.90-0.99, MODIS: 0.74-0.85) between most simulated bands and observed bands and indicated that the simulated reflectance spectrum was well simulated and reliable. PMID:26717721

  15. Design of partially supervised classifiers for multispectral image data

    NASA Technical Reports Server (NTRS)

    Jeon, Byeungwoo; Landgrebe, David

    1993-01-01

    A partially supervised classification problem is addressed, especially when the class definition and corresponding training samples are provided a priori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class characteristics of all classes in a given data set. Considering the effort in both time and man-power required to have a well-defined, exhaustive list of classes with a corresponding representative set of training samples, this 'partially' supervised capability would be very desirable, assuming adequate classifier performance can be obtained. Two different classification algorithms are developed to achieve simplicity in classifier design by reducing the requirement of prior statistical information without sacrificing significant classifying capability. The first one is based on optimal significance testing, where the optimal acceptance probability is estimated directly from the data set. In the second approach, the partially supervised classification is considered as a problem of unsupervised clustering with initially one known cluster or class. A weighted unsupervised clustering procedure is developed to automatically define other classes and estimate their class statistics. The operational simplicity thus realized should make these partially supervised classification schemes very viable tools in pattern classification.

  16. MULTISPECTRAL THERMAL IMAGER SCIENCE, DATA PRODUCT AND GROUND DATA PROCESSING OVERVIEW.

    SciTech Connect

    J. SZYMANSKI; L. BALICK; ET AL

    2001-04-01

    The mission of the Multispectral Thermal Imager (MTI) satellite is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of urban and industrial areas, as well as sites of environmental interest. The satellite makes top-of-atmosphere radiance measurements that are subsequently processed into estimates of surface properties such as vegetation health, temperatures, material composition and others. The system also provides simultaneous data for atmospheric characterization at high spatial resolution. To utilize these data the MTI science program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data analysis and interpretation . Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and programmability. This paper describes the MTI data products and ground processing, as well as the ''how to'' aspects of starting a data center from scratch.

  17. Digital staining for histopathology multispectral images by the combined application of spectral enhancement and spectral transformation.

    PubMed

    Bautista, Pinky A; Yagi, Yukako

    2011-01-01

    In this paper we introduced a digital staining method for histopathology images captured with an n-band multispectral camera. The method consisted of two major processes: enhancement of the original spectral transmittance and the transformation of the enhanced transmittance to its target spectral configuration. Enhancement is accomplished by shifting the original transmittance with the scaled difference between the original transmittance and the transmittance estimated with m dominant principal component (PC) vectors;the m-PC vectors were determined from the transmittance samples of the background image. Transformation of the enhanced transmittance to the target spectral configuration was done using an nxn transformation matrix, which was derived by applying a least square method to the enhanced and target spectral training data samples of the different tissue components. Experimental results on the digital conversion of a hematoxylin and eosin (H&E) stained multispectral image to its Masson's trichrome stained (MT) equivalent shows the viability of the method.

  18. Workshop on the Use of Future Multispectral Imaging Capabilities for Lithologic Mapping: Workshop summary

    NASA Technical Reports Server (NTRS)

    Settle, M.; Adams, J.

    1982-01-01

    Improved orbital imaging capabilities from the standpoint of different scientific disciplines, such as geology, botany, hydrology, and geography were evaluated. A discussion on how geologists might exploit the anticipated measurement capabilities of future orbital imaging systems to discriminate and characterize different types of geologic materials exposed at the Earth's surface is presented. Principle objectives are to summarize past accomplishments in the use of multispectral imaging techniques for lithologic mapping; to identify critical gaps in earlier research efforts that currently limit the ability to extract useful information about the physical and chemical characteristics of geological materials from orbital multispectral surveys; and to define major thresholds, resolution and sensitivity within the visible and infrared portions of the electromagnetic spectrum which, if achieved would result in significant improvement in our ability to discriminate and characterize different geological materials exposed at the Earth's surface.

  19. Multispectral Airborne Laser Scanning for Automated Map Updating

    NASA Astrophysics Data System (ADS)

    Matikainen, Leena; Hyyppä, Juha; Litkey, Paula

    2016-06-01

    During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

  20. Multispectral image fusion based on diffusion morphology for enhanced vision applications

    NASA Astrophysics Data System (ADS)

    Knyaz, Vladimir A.; Vygolov, Oleg V.; Vizilter, Yury V.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

    2016-05-01

    Existing image fusion methods based on morphological image analysis, that expresses the geometrical idea of image shape as a label image, are quite sensitive to the quality of image segmentation and, therefore, not sufficiently robust to noise and high frequency distortions. On the other hand, there are a number of methods in the field of dimensionality reduction and data comparison that give possibility of avoiding an image segmentation step by using diffusion maps techniques. The paper proposes a new approach for multispectral image fusion based on the combination of morphological image analysis and diffusion maps theory (i.e. Diffusion Morphology). A new image fusion algorithm is described that uses a matched diffusion filtering procedure instead of morphological projection. The algorithm is implemented for a three channels Enhanced Vision System prototype. The comparative results of image fusion are shown on real images acquired in flight experiments.

  1. Development of a portable 3CCD camera system for multispectral imaging of biological samples.

    PubMed

    Lee, Hoyoung; Park, Soo Hyun; Noh, Sang Ha; Lim, Jongguk; Kim, Moon S

    2014-01-01

    Recent studies have suggested the need for imaging devices capable of multispectral imaging beyond the visible region, to allow for quality and safety evaluations of agricultural commodities. Conventional multispectral imaging devices lack flexibility in spectral waveband selectivity for such applications. In this paper, a recently developed portable 3CCD camera with significant improvements over existing imaging devices is presented. A beam-splitter prism assembly for 3CCD was designed to accommodate three interference filters that can be easily changed for application-specific multispectral waveband selection in the 400 to 1000 nm region. We also designed and integrated electronic components on printed circuit boards with firmware programming, enabling parallel processing, synchronization, and independent control of the three CCD sensors, to ensure the transfer of data without significant delay or data loss due to buffering. The system can stream 30 frames (3-waveband images in each frame) per second. The potential utility of the 3CCD camera system was demonstrated in the laboratory for detecting defect spots on apples.

  2. Development of a portable 3CCD camera system for multispectral imaging of biological samples.

    PubMed

    Lee, Hoyoung; Park, Soo Hyun; Noh, Sang Ha; Lim, Jongguk; Kim, Moon S

    2014-01-01

    Recent studies have suggested the need for imaging devices capable of multispectral imaging beyond the visible region, to allow for quality and safety evaluations of agricultural commodities. Conventional multispectral imaging devices lack flexibility in spectral waveband selectivity for such applications. In this paper, a recently developed portable 3CCD camera with significant improvements over existing imaging devices is presented. A beam-splitter prism assembly for 3CCD was designed to accommodate three interference filters that can be easily changed for application-specific multispectral waveband selection in the 400 to 1000 nm region. We also designed and integrated electronic components on printed circuit boards with firmware programming, enabling parallel processing, synchronization, and independent control of the three CCD sensors, to ensure the transfer of data without significant delay or data loss due to buffering. The system can stream 30 frames (3-waveband images in each frame) per second. The potential utility of the 3CCD camera system was demonstrated in the laboratory for detecting defect spots on apples. PMID:25350510

  3. Development of a Portable 3CCD Camera System for Multispectral Imaging of Biological Samples

    PubMed Central

    Lee, Hoyoung; Park, Soo Hyun; Noh, Sang Ha; Lim, Jongguk; Kim, Moon S.

    2014-01-01

    Recent studies have suggested the need for imaging devices capable of multispectral imaging beyond the visible region, to allow for quality and safety evaluations of agricultural commodities. Conventional multispectral imaging devices lack flexibility in spectral waveband selectivity for such applications. In this paper, a recently developed portable 3CCD camera with significant improvements over existing imaging devices is presented. A beam-splitter prism assembly for 3CCD was designed to accommodate three interference filters that can be easily changed for application-specific multispectral waveband selection in the 400 to 1000 nm region. We also designed and integrated electronic components on printed circuit boards with firmware programming, enabling parallel processing, synchronization, and independent control of the three CCD sensors, to ensure the transfer of data without significant delay or data loss due to buffering. The system can stream 30 frames (3-waveband images in each frame) per second. The potential utility of the 3CCD camera system was demonstrated in the laboratory for detecting defect spots on apples. PMID:25350510

  4. Improving image classification in a complex wetland ecosystem through image fusion techniques

    NASA Astrophysics Data System (ADS)

    Kumar, Lalit; Sinha, Priyakant; Taylor, Subhashni

    2014-01-01

    The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram-Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram-Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram-Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.

  5. A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters

    PubMed Central

    Li, Hanlun; Zhang, Aiwu; Hu, Shaoxing

    2015-01-01

    This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels. PMID:26205264

  6. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data

    NASA Technical Reports Server (NTRS)

    Shimabukuro, Yosio Edemir; Smith, James A.

    1991-01-01

    Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.

  7. Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology.

    PubMed

    Duliu, Alexandru; Brosig, Richard; Ognawala, Saahil; Lasser, Tobias; Ziai, Mahzad; Navab, Nassir

    2015-01-01

    When attempting to recover the surface color from an image, modelling the illumination contribution per-pixel is essential. In this work we present a novel approach for illumination compensation using multispectral image data. This is done by means of a low-rank decomposition of representative spectral bands with prior knowledge of the reflectance spectra of the imaged surface. Experimental results on synthetic data, as well as on images of real lesions acquired at the university clinic, show that the proposed method significantly improves the contrast between the lesion and the background.

  8. A multispectral FLIM tomograph for in-vivo imaging of skin cancer

    NASA Astrophysics Data System (ADS)

    Talbot, C. B.; Patalay, R.; Munro, I.; Breunig, H. G.; König, K.; Alexandrov, Y.; Warren, S.; Chu, A.; Stamp, G. W.; Neil, M. A. A.; French, Paul M. W.; Dunsby, Christopher

    2011-03-01

    To aid the in vivo diagnosis of skin lesions, we present the design and implementation of a 4 channel FLIM detector and a hyperspectral imaging detector into a clinically licensed commercial two-photon tomograph. We have also implemented image segmentation algorithms to facilitate the automated processing of the large volumes of data produced. The first detector is based on multispectral time correlated single photon counting, providing four channel fluorescence lifetime images. The second detector is a prism-based CCD hyperspectral imager. These detectors provide the capability to extract the relative content and state of autofluorescence compounds present in biological tissue.

  9. Multispectral imaging of papyri: area segregation by evaluation of their spectral signature correlation

    NASA Astrophysics Data System (ADS)

    Kovalchuk, Alexander

    2009-10-01

    Multispectral imaging technology was applied to deteriorated ancient manuscripts. Monochrome images were registered at twelve wavelengths. An image processing algorithm that utilises relative spectral intensity distribution for the areas of the surface (rather than absolute values of intensity of the reflected light) was evaluated. It was found that the areas in a cubed MSI image representing ink and papyrus were separable due to their distinctive spectral signatures. The developed technique for enhancing the contrast of the inscriptions was shown to work effectively over a large range of lightness for both ink and papyrus surface.

  10. Classification of land cover from remote sensing fused image based on ICA-SVM and D-S evidence theory

    NASA Astrophysics Data System (ADS)

    Chen, Mi; Fu, Yingchun; Sun, Tao; Li, Deren; Qin, Qianqing

    2008-10-01

    Remote sensing image classification is an important means for quantified remote sensing image analysis, and remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes a classification algorithm of remote sensing fused images based on independent component analysis (ICA), topographic independent component analysis (TICA), support vector machines (SVMs) and D-S evidence theory. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions and shapes than wavelet. As independent component analysis not only can effectively remove the correlation of multi-spectral images, but also can realize sparse coding of images and capture the essential edge structures and textures of images, then using features extracted from the ICA and TICA domain coefficients of the fused images, the SVMs are trained to classify the whole fused images. Finally apply the proposed novel D-S evidence combination scheme to make decision fusion for different classification results with different features obtained by SVMs. Experimental results show that the proposed algorithm can effectively improve the accuracy of image classification.

  11. Nightfire method to track volcanic eruptions from multispectral satellite images

    NASA Astrophysics Data System (ADS)

    Trifonov, Grigory; Zhizhin, Mikhail; Melnikov, Dmitry

    2016-04-01

    This work presents the first results of an application of the Nightfire hotspot algorithm towards volcano activity detection. Nightfire algorithm have been developed to play along with a Suomi-NPP polar satellite launched in 2011, which has a new generation multispectral VIIRS thermal sensor on board, to detect gas flares related to the upstream and downstream production of oil and natural gas. Simultaneously using of nighttime data in SWIR, MWIR, and LWIR sensor bands the algorithm is able to estimate the hotspot temperature, size and radiant heat. Four years of non-filtered observations have been accumulated in a spatio-temporal detection database, which currently totals 125 GB in size. The first part of this work presents results of retrospective cross-match of the detection database with the publicly available observed eruptions databases. The second part discusses how an approximate 3D shape of a lava lake could be modeled based on the apparent source size and satellite zenith angle. The third part presents the results of fusion Landsat-8 and Himawari-8 satellites data with the VIIRS Nightfire for several active volcanoes.

  12. Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance.

    PubMed

    Bautista, Pinky A; Yagi, Yukako

    2012-05-01

    Hematoxylin and eosin (H&E) stain is currently the most popular for routine histopathology staining. Special and/or immuno-histochemical (IHC) staining is often requested to further corroborate the initial diagnosis on H&E stained tissue sections. Digital simulation of staining (or digital staining) can be a very valuable tool to produce the desired stained images from the H&E stained tissue sections instantaneously. We present an approach to digital staining of histopathology multispectral images by combining the effects of spectral enhancement and spectral transformation. Spectral enhancement is accomplished by shifting the N-band original spectrum of the multispectral pixel with the weighted difference between the pixel's original and estimated spectrum; the spectrum is estimated using M < N principal component (PC) vectors. The pixel's enhanced spectrum is transformed to the spectral configuration associated to its reaction to a specific stain by utilizing an N × N transformation matrix, which is derived through application of least mean squares method to the enhanced and target spectral transmittance samples of the different tissue components found in the image. Results of our experiments on the digital conversion of an H&E stained multispectral image to its Masson's trichrome stained equivalent show the viability of the method.

  13. Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance

    NASA Astrophysics Data System (ADS)

    Bautista, Pinky A.; Yagi, Yukako

    2012-05-01

    Hematoxylin and eosin (H&E) stain is currently the most popular for routine histopathology staining. Special and/or immuno-histochemical (IHC) staining is often requested to further corroborate the initial diagnosis on H&E stained tissue sections. Digital simulation of staining (or digital staining) can be a very valuable tool to produce the desired stained images from the H&E stained tissue sections instantaneously. We present an approach to digital staining of histopathology multispectral images by combining the effects of spectral enhancement and spectral transformation. Spectral enhancement is accomplished by shifting the N-band original spectrum of the multispectral pixel with the weighted difference between the pixel's original and estimated spectrum; the spectrum is estimated using M < N principal component (PC) vectors. The pixel's enhanced spectrum is transformed to the spectral configuration associated to its reaction to a specific stain by utilizing an N × N transformation matrix, which is derived through application of least mean squares method to the enhanced and target spectral transmittance samples of the different tissue components found in the image. Results of our experiments on the digital conversion of an H&E stained multispectral image to its Masson's trichrome stained equivalent show the viability of the method.

  14. Two-port multispectral imaging system for contaminant detection on poultry carcasses

    NASA Astrophysics Data System (ADS)

    Kise, Michio; Park, Bosoon; Lawrence, Kurt C.; Windham, Robert R.

    2006-10-01

    The objective of this research is to design and fabricate a compact, cost effective multispectral instrument and to collect and analyze spectra for real-time contaminant detection for poultry processing plants. It was revealed by our previous research that the fecal contamination on the surface of the poultry carcass could be detected by sensing the spectral reflectance of the carcass surface in two specific wavelengths, namely 517 nm and 565 nm. The prototype system developed in this research consists of a multispectral imaging system, illumination system and handheld PC. To develop the system cost-effectively, all components are selected from off-the-shelf products and manually assembled. The multispectral imaging sensor developed in this research is a two-port imaging system that consists of two identical monochrome cameras, optical system and two narrow bandpass filters whose center of the wavelength are 520 and 560 nm, respectively. A spectral reflectance from a chicken carcass is collected and split in two directions by an optical system including a beamsplitter and lenses, and then two identical collimated lights are filtered by the narrow bandpass filters and delivered to the cameras. Lens distortions and geometric misalignment of the two cameras are mathematically compensated to register two images perfectly. The prototype system is tested in the real environment and shows that it can effectively detect feces and ingesta on the surface of poultry carcasses.

  15. Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance.

    PubMed

    Bautista, Pinky A; Yagi, Yukako

    2012-05-01

    Hematoxylin and eosin (H&E) stain is currently the most popular for routine histopathology staining. Special and/or immuno-histochemical (IHC) staining is often requested to further corroborate the initial diagnosis on H&E stained tissue sections. Digital simulation of staining (or digital staining) can be a very valuable tool to produce the desired stained images from the H&E stained tissue sections instantaneously. We present an approach to digital staining of histopathology multispectral images by combining the effects of spectral enhancement and spectral transformation. Spectral enhancement is accomplished by shifting the N-band original spectrum of the multispectral pixel with the weighted difference between the pixel's original and estimated spectrum; the spectrum is estimated using M < N principal component (PC) vectors. The pixel's enhanced spectrum is transformed to the spectral configuration associated to its reaction to a specific stain by utilizing an N × N transformation matrix, which is derived through application of least mean squares method to the enhanced and target spectral transmittance samples of the different tissue components found in the image. Results of our experiments on the digital conversion of an H&E stained multispectral image to its Masson's trichrome stained equivalent show the viability of the method. PMID:22612136

  16. Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral Imaging

    PubMed Central

    Thatcher, Jeffrey E.; Squiers, John J.; Kanick, Stephen C.; King, Darlene R.; Lu, Yang; Wang, Yulin; Mohan, Rachit; Sellke, Eric W.; DiMaio, J. Michael

    2016-01-01

    Significance: Burn assessments, including extent and severity, are some of the most critical diagnoses in burn care, and many recently developed imaging techniques may have the potential to improve the accuracy of these evaluations. Recent Advances: Optical devices, telemedicine, and high-frequency ultrasound are among the highlights in recent burn imaging advancements. We present another promising technology, multispectral imaging (MSI), which also has the potential to impact current medical practice in burn care, among a variety of other specialties. Critical Issues: At this time, it is still a matter of debate as to why there is no consensus on the use of technology to assist burn assessments in the United States. Fortunately, the availability of techniques does not appear to be a limitation. However, the selection of appropriate imaging technology to augment the provision of burn care can be difficult for clinicians to navigate. There are many technologies available, but a comprehensive review summarizing the tissue characteristics measured by each technology in light of aiding clinicians in selecting the proper device is missing. This would be especially valuable for the nonburn specialists who encounter burn injuries. Future Directions: The questions of when burn assessment devices are useful to the burn team, how the various imaging devices work, and where the various burn imaging technologies fit into the spectrum of burn care will continue to be addressed. Technologies that can image a large surface area quickly, such as thermography or laser speckle imaging, may be suitable for initial burn assessment and triage. In the setting of presurgical planning, ultrasound or optical microscopy techniques, including optical coherence tomography, may prove useful. MSI, which actually has origins in burn care, may ultimately meet a high number of requirements for burn assessment in routine clinical use. PMID:27602255

  17. Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral Imaging

    PubMed Central

    Thatcher, Jeffrey E.; Squiers, John J.; Kanick, Stephen C.; King, Darlene R.; Lu, Yang; Wang, Yulin; Mohan, Rachit; Sellke, Eric W.; DiMaio, J. Michael

    2016-01-01

    Significance: Burn assessments, including extent and severity, are some of the most critical diagnoses in burn care, and many recently developed imaging techniques may have the potential to improve the accuracy of these evaluations. Recent Advances: Optical devices, telemedicine, and high-frequency ultrasound are among the highlights in recent burn imaging advancements. We present another promising technology, multispectral imaging (MSI), which also has the potential to impact current medical practice in burn care, among a variety of other specialties. Critical Issues: At this time, it is still a matter of debate as to why there is no consensus on the use of technology to assist burn assessments in the United States. Fortunately, the availability of techniques does not appear to be a limitation. However, the selection of appropriate imaging technology to augment the provision of burn care can be difficult for clinicians to navigate. There are many technologies available, but a comprehensive review summarizing the tissue characteristics measured by each technology in light of aiding clinicians in selecting the proper device is missing. This would be especially valuable for the nonburn specialists who encounter burn injuries. Future Directions: The questions of when burn assessment devices are useful to the burn team, how the various imaging devices work, and where the various burn imaging technologies fit into the spectrum of burn care will continue to be addressed. Technologies that can image a large surface area quickly, such as thermography or laser speckle imaging, may be suitable for initial burn assessment and triage. In the setting of presurgical planning, ultrasound or optical microscopy techniques, including optical coherence tomography, may prove useful. MSI, which actually has origins in burn care, may ultimately meet a high number of requirements for burn assessment in routine clinical use.

  18. Land use/cover classification in the Brazilian Amazon using satellite images

    PubMed Central

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

    2013-01-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

  19. Land use/cover classification in the Brazilian Amazon using satellite images.

    PubMed

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

    2012-09-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

  20. PDE-constrained multispectral imaging of tissue chromophores with the equation of radiative transfer

    PubMed Central

    Kim, Hyun Keol; Flexman, Molly; Yamashiro, Darrell J.; Kandel, Jessica J.; Hielscher, Andreas H.

    2010-01-01

    We introduce a transport-theory-based PDE-constrained multispectral model for direct imaging of the spatial distributions of chromophores concentrations in biological tissue. The method solves the forward problem (boundary radiance at each wavelength) and the inverse problem (spatial distribution of chromophores concentrations), in an all-at-once manner in the framework of a reduced Hessian sequential quadratic programming method. To illustrate the code’s performance, we present numerical and experimental studies involving tumor bearing mice. It is shown that the PDE-constrained multispectral method accelerates the reconstruction process by up to 15 times compared to unconstrained reconstruction algorithms and provides more accurate results as compared to the so-called two-step approach to multi-wavelength imaging. PMID:21258511

  1. Depth-Resolved Multispectral Sub-Surface Imaging Using Multifunctional Upconversion Phosphors with Paramagnetic Properties

    PubMed Central

    Ovanesyan, Zaven; Mimun, L. Christopher; Kumar, Gangadharan Ajith; Yust, Brian G.; Dannangoda, Chamath; Martirosyan, Karen S.; Sardar, Dhiraj K.

    2015-01-01

    Molecular imaging is very promising technique used for surgical guidance, which requires advancements related to properties of imaging agents and subsequent data retrieval methods from measured multispectral images. In this article, an upconversion material is introduced for subsurface near-infrared imaging and for the depth recovery of the material embedded below the biological tissue. The results confirm significant correlation between the analytical depth estimate of the material under the tissue and the measured ratio of emitted light from the material at two different wavelengths. Experiments with biological tissue samples demonstrate depth resolved imaging using the rare earth doped multifunctional phosphors. In vitro tests reveal no significant toxicity, whereas the magnetic measurements of the phosphors show that the particles are suitable as magnetic resonance imaging agents. The confocal imaging of fibroblast cells with these phosphors reveals their potential for in vivo imaging. The depth-resolved imaging technique with such phosphors has broad implications for real-time intraoperative surgical guidance. PMID:26322519

  2. Viola-Jones based hybrid framework for real-time object detection in multispectral images

    NASA Astrophysics Data System (ADS)

    Kuznetsova, E.; Shvets, E.; Nikolaev, D.

    2015-12-01

    This paper describes a method for real-time object detection based on a hybrid of a Viola-Jones cascade with a convolutional neural network. This scheme allows flexible trade-offs between detection quality and computational performance. We also propose a generalization of this method to multispectral images that effectively and efficiently utilizes information from each spectral channel. The new scheme is experimentally compared to traditional Viola-Jones, showing improved detection quality with adjustable performance.

  3. Science Applications of a Multispectral Microscopic Imager for the Astrobiological Exploration of Mars

    PubMed Central

    Farmer, Jack D.; Sellar, R. Glenn; Swayze, Gregg A.; Blaney, Diana L.

    2014-01-01

    Abstract Future astrobiological missions to Mars are likely to emphasize the use of rovers with in situ petrologic capabilities for selecting the best samples at a site for in situ analysis with onboard lab instruments or for caching for potential return to Earth. Such observations are central to an understanding of the potential for past habitable conditions at a site and for identifying samples most likely to harbor fossil biosignatures. The Multispectral Microscopic Imager (MMI) provides multispectral reflectance images of geological samples at the microscale, where each image pixel is composed of a visible/shortwave infrared spectrum ranging from 0.46 to 1.73 μm. This spectral range enables the discrimination of a wide variety of rock-forming minerals, especially Fe-bearing phases, and the detection of hydrated minerals. The MMI advances beyond the capabilities of current microimagers on Mars by extending the spectral range into the infrared and increasing the number of spectral bands. The design employs multispectral light-emitting diodes and an uncooled indium gallium arsenide focal plane array to achieve a very low mass and high reliability. To better understand and demonstrate the capabilities of the MMI for future surface missions to Mars, we analyzed samples from Mars-relevant analog environments with the MMI. Results indicate that the MMI images faithfully resolve the fine-scale microtextural features of samples and provide important information to help constrain mineral composition. The use of spectral endmember mapping reveals the distribution of Fe-bearing minerals (including silicates and oxides) with high fidelity, along with the presence of hydrated minerals. MMI-based petrogenetic interpretations compare favorably with laboratory-based analyses, revealing the value of the MMI for future in situ rover-mediated astrobiological exploration of Mars. Key Words: Mars—Microscopic imager—Multispectral imaging

  4. Multispectral imaging with optical bandpass filters: tilt angle and position estimation

    NASA Astrophysics Data System (ADS)

    Brauers, Johannes; Aach, Til

    2009-01-01

    Optical bandpass filters play a decisive role in multispectral imaging. Various multispectral cameras use this type of color filter for the sequential acquisition of different spectral bands. Practically unavoidable, small tilt angles of the filters with respect to the optical axis influence the imaging process: First, by tilting the filter, the center wavelength of the filter is shifted, causing color variations. Second, due to refractions of the filter, the image is distorted geometrically depending on the tilt angle. Third, reflections between sensor and filter glass may cause ghosting, i.e., a weak and shifted copy of the image, which also depends on the filter angle. A method to measure the filter position parameters from multispectral color components is thus highly desirable. We propose a method to determine the angle and position of an optical filter brought into the optical path in, e.g., filter-wheel multispectral cameras, with respect to the camera coordinate system. We determine the position and angle of the filter by presenting a calibration chart to the camera, which is always partly reflected by the highly reflective optical bandpass filter. The extrinsic parameters of the original and mirrored chart can then be estimated. We derive the angle and position of the filter from the coordinates of the charts. We compare the results of the angle measurements to a ground truth obtained from the settings of a high-precision rotation table and thus validate our measurement method. Furthermore, we simulate the refraction effect of the optical filter and show that the results match quite well with the reality, thus also confirming our method.

  5. Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

    2013-11-01

    Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

  6. Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

    NASA Astrophysics Data System (ADS)

    Anderson, Ryan B.; Bell, James F.

    2013-03-01

    In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red-blue ratio (673 nm/434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400-1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows that

  7. Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

    USGS Publications Warehouse

    Anderson, Ryan B.; Bell, James F.

    2013-01-01

    In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red–blue ratio (673 nm/434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400–1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows

  8. Processing Of Multispectral Data For Identification Of Rocks

    NASA Technical Reports Server (NTRS)

    Evans, Diane L.

    1990-01-01

    Linear discriminant analysis and supervised classification evaluated. Report discusses processing of multispectral remote-sensing imagery to identify kinds of sedimentary rocks by spectral signatures in geological and geographical contexts. Raw image data are spectra of picture elements in images of seven sedimentary rock units exposed on margin of Wind River Basin in Wyoming. Data acquired by Landsat Thematic Mapper (TM), Thermal Infrared Multispectral Scanner (TIMS), and NASA/JPL airborne synthetic-aperture radar (SAR).

  9. Real-time multispectral imaging system for online poultry fecal inspection using UML

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Kise, Michio; Lawrence, Kurt C.; Windham, William R.; Smith, Douglas P.; Thai, Chi N.

    2006-10-01

    A prototype real-time multispectral imaging system for fecal and ingesta contaminant detection on broiler carcasses has been developed. The prototype system includes a common aperture camera with three optical trim filters (517, 565 and 802-nm wavelength), which were selected by visible/NIR spectroscopy and validated by a hyperspectral imaging system with decision tree algorithm. The on-line testing results showed that the multispectral imaging technique can be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses with a processing speed of 140 birds per minute. This paper demonstrated both multispectral imaging hardware and real-time image processing software. For the software development, the Unified Modeling Language (UML) design approach was used for on-line application. The UML models included class, object, activity, sequence, and collaboration diagram. User interface model included seventeen inputs and six outputs. A window based real-time image processing software composed of eleven components, which represented class, architecture, and activity. Both hardware and software for a real-time fecal detection were tested at the pilot-scale poultry processing plant. The run-time of the software including online calibration was fast enough to inspect carcasses on-line with an industry requirement. Based on the preliminary test at the pilot-scale processing line, the system was able to acquire poultry images in real-time. According to the test results, the imaging system is reliable for the harsh environments and UML based image processing software is flexible and easy to be updated when additional parameters are needed for in-plant trials.

  10. High performance multi-spectral interrogation for surface plasmon resonance imaging sensors.

    PubMed

    Sereda, A; Moreau, J; Canva, M; Maillart, E

    2014-04-15

    Surface plasmon resonance (SPR) sensing has proven to be a valuable tool in the field of surface interactions characterization, especially for biomedical applications where label-free techniques are of particular interest. In order to approach the theoretical resolution limit, most SPR-based systems have turned to either angular or spectral interrogation modes, which both offer very accurate real-time measurements, but at the expense of the 2-dimensional imaging capability, therefore decreasing the data throughput. In this article, we show numerically and experimentally how to combine the multi-spectral interrogation technique with 2D-imaging, while finding an optimum in terms of resolution, accuracy, acquisition speed and reduction in data dispersion with respect to the classical reflectivity interrogation mode. This multi-spectral interrogation methodology is based on a robust five parameter fitting of the spectral reflectivity curve which enables monitoring of the reflectivity spectral shift with a resolution of the order of ten picometers, and using only five wavelength measurements per point. In fine, such multi-spectral based plasmonic imaging system allows biomolecular interaction monitoring in a linear regime independently of variations of buffer optical index, which is illustrated on a DNA-DNA model case.

  11. Generation of multi-spectral scene images under different weather conditions

    NASA Astrophysics Data System (ADS)

    Wang, Changbo; Wang, Zhangye; Wu, Yiqi; Peng, Qunsheng

    2005-10-01

    Different kinds of atmospheric media and variation of weather conditions make the generation of multi-spectral out-door scene image quite complex. In this paper, we propose a new approach to realistically generate the multi-spectral scene images under different weather conditions. We first establish the geometrical models of several special weather phenomena. The method of three-dimension cellular automata is adopted to construct the cloud, and particle system is used to model the rain. After that we calculate the radiance of different wavebands for cloud, fog, rain under different weather conditions. The radiance components, such as self-emitted radiance, reflected radiation, transmitted radiance, are calculated separately and the total radiance is thus acquired. Then, based on the spectral radiance values of cloud, rain and fog calculated by above models, we construct the scene under different weather conditions. Finally multi-spectral scene images under different weather conditions are rendered realistically. Simulation results show the potential of our approach.

  12. Hemodynamic and morphologic responses in mouse brain during acute head injury imaged by multispectral structured illumination

    NASA Astrophysics Data System (ADS)

    Volkov, Boris; Mathews, Marlon S.; Abookasis, David

    2015-03-01

    Multispectral imaging has received significant attention over the last decade as it integrates spectroscopy, imaging, tomography analysis concurrently to acquire both spatial and spectral information from biological tissue. In the present study, a multispectral setup based on projection of structured illumination at several near-infrared wavelengths and at different spatial frequencies is applied to quantitatively assess brain function before, during, and after the onset of traumatic brain injury in an intact mouse brain (n=5). For the production of head injury, we used the weight drop method where weight of a cylindrical metallic rod falling along a metal tube strikes the mouse's head. Structured light was projected onto the scalp surface and diffuse reflected light was recorded by a CCD camera positioned perpendicular to the mouse head. Following data analysis, we were able to concurrently show a series of hemodynamic and morphologic changes over time including higher deoxyhemoglobin, reduction in oxygen saturation, cell swelling, etc., in comparison with baseline measurements. Overall, results demonstrates the capability of multispectral imaging based structured illumination to detect and map of brain tissue optical and physiological properties following brain injury in a simple noninvasive and noncontact manner.

  13. Iterative normalization method for improved prostate cancer localization with multispectral magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Liu, Xin; Samil Yetik, Imam

    2012-04-01

    Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.

  14. Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery

    NASA Astrophysics Data System (ADS)

    Gerstmann, Henning; Möller, Markus; Gläßer, Cornelia

    2016-10-01

    Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η2 (eta-squared) measure of association - presented as novel spectral separability indicator - was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010-2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.

  15. An interventional multispectral photoacoustic imaging platform for the guidance of minimally invasive procedures

    NASA Astrophysics Data System (ADS)

    Xia, Wenfeng; Nikitichev, Daniil I.; Mari, Jean Martial; West, Simeon J.; Ourselin, Sebastien; Beard, Paul C.; Desjardins, Adrien E.

    2015-07-01

    Precise and efficient guidance of medical devices is of paramount importance for many minimally invasive procedures. These procedures include fetal interventions, tumor biopsies and treatments, central venous catheterisations and peripheral nerve blocks. Ultrasound imaging is commonly used for guidance, but it often provides insufficient contrast with which to identify soft tissue structures such as vessels, tumors, and nerves. In this study, a hybrid interventional imaging system that combines ultrasound imaging and multispectral photoacoustic imaging for guiding minimally invasive procedures was developed and characterized. The system provides both structural information from ultrasound imaging and molecular information from multispectral photoacoustic imaging. It uses a commercial linear-array ultrasound imaging probe as the ultrasound receiver, with a multimode optical fiber embedded in a needle to deliver pulsed excitation light to tissue. Co-registration of ultrasound and photoacoustic images is achieved with the use of the same ultrasound receiver for both modalities. Using tissue ex vivo, the system successfully discriminated deep-located fat tissue from the surrounding muscle tissue. The measured photoacoustic spectrum of the fat tissue had good agreement with the lipid spectrum in literature.

  16. Multispectral/hyperspectral image enhancement for biological cell analysis

    SciTech Connect

    Nuffer, Lisa L.; Medvick, Patricia A.; Foote, Harlan P.; Solinsky, James C.

    2006-08-01

    The paper shows new techniques for analyzing cell images taken with a microscope using multiple filters to form a datacube of spectral image planes. Because of the many neighboring spectral samples, much of the datacube appears as redundant, similar tissue. The analysis is based on the nonGaussian statistics of the image data, allowing for remapping of the data into image components that are dissimilar, and hence isolate subtle, spatial object regions of interest in the tissues. This individual component image set can be recombined into a single RGB color image useful in real-time location of regions of interest. The algorithms are susceptible to parallelization using Field Programmable Gate Array hardware processing.

  17. Comparing Individual Tree Segmentation Based on High Resolution Multispectral Image and Lidar Data

    NASA Astrophysics Data System (ADS)

    Xiao, P.; Kelly, M.; Guo, Q.

    2014-12-01

    This study compares the use of high-resolution multispectral WorldView images and high density Lidar data for individual tree segmentation. The application focuses on coniferous and deciduous forests in the Sierra Nevada Mountains. The tree objects are obtained in two ways: a hybrid region-merging segmentation method with multispectral images, and a top-down and bottom-up region-growing method with Lidar data. The hybrid region-merging method is used to segment individual tree from multispectral images. It integrates the advantages of global-oriented and local-oriented region-merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region. The merging iterations are constrained within the local vicinity, thus the segmentation is accelerated and can reflect the local context. The top-down region-growing method is adopted in coniferous forest to delineate individual tree from Lidar data. It exploits the spacing between the tops of trees to identify and group points into a single tree based on simple rules of proximity and likely tree shape. The bottom-up region-growing method based on the intensity and 3D structure of Lidar data is applied in deciduous forest. It segments tree trunks based on the intensity and topological relationships of the points, and then allocate other points to exact tree crowns according to distance. The accuracies for each method are evaluated with field survey data in several test sites, covering dense and sparse canopy. Three types of segmentation results are produced: true positive represents a correctly segmented individual tree, false negative represents a tree that is not detected and assigned to a nearby tree, and false positive represents that a point or pixel cluster is segmented as a tree that does not in fact exist. They respectively represent correct-, under-, and over-segmentation. Three types of index are compared for segmenting individual tree

  18. LED lighting for use in multispectral and hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Lighting for machine vision and hyperspectral imaging is an important component for collecting high quality imagery. However, it is often given minimal consideration in the overall design of an imaging system. Tungsten-halogens lamps are the most common source of illumination for broad spectrum appl...

  19. Accurate statistical tests for smooth classification images.

    PubMed

    Chauvin, Alan; Worsley, Keith J; Schyns, Philippe G; Arguin, Martin; Gosselin, Frédéric

    2005-10-05

    Despite an obvious demand for a variety of statistical tests adapted to classification images, few have been proposed. We argue that two statistical tests based on random field theory (RFT) satisfy this need for smooth classification images. We illustrate these tests on classification images representative of the literature from F. Gosselin and P. G. Schyns (2001) and from A. B. Sekuler, C. M. Gaspar, J. M. Gold, and P. J. Bennett (2004). The necessary computations are performed using the Stat4Ci Matlab toolbox.

  20. Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging.

    PubMed

    Pan, Wen-Juan; Wang, Xia; Deng, Yong-Ren; Li, Jia-Hang; Chen, Wei; Chiang, John Y; Yang, Jian-Bo; Zheng, Lei

    2015-06-10

    The circadian clock, synchronized by daily cyclic environmental cues, regulates diverse aspects of plant growth and development and increases plant fitness. Even though much is known regarding the molecular mechanism of circadian clock, it remains challenging to quantify the temporal variation of major photosynthesis products as well as their metabolic output in higher plants in a real-time, nondestructive and intuitive manner. In order to reveal the spatial-temporal scenarios of photosynthesis and yield formation regulated by circadian clock, multispectral imaging technique has been employed for nondestructive determination of circadian chlorophyll rhythms in soybean leaves. By utilizing partial least square regression analysis, the determination coefficients R(2), 0.9483 for chlorophyll a and 0.8906 for chlorophyll b, were reached, respectively. The predicted chlorophyll contents extracted from multispectral data showed an approximately 24-h rhythm which could be entrained by external light conditions, consistent with the chlorophyll contents measured by chemical analyses. Visualization of chlorophyll map in each pixel offers an effective way to analyse spatial-temporal distribution of chlorophyll. Our results revealed the potentiality of multispectral imaging as a feasible nondestructive universal assay for examining clock function and robustness, as well as monitoring chlorophyll a and b and other biochemical components in plants.

  1. Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Pan, Wen-Juan; Wang, Xia; Deng, Yong-Ren; Li, Jia-Hang; Chen, Wei; Chiang, John Y.; Yang, Jian-Bo; Zheng, Lei

    2015-06-01

    The circadian clock, synchronized by daily cyclic environmental cues, regulates diverse aspects of plant growth and development and increases plant fitness. Even though much is known regarding the molecular mechanism of circadian clock, it remains challenging to quantify the temporal variation of major photosynthesis products as well as their metabolic output in higher plants in a real-time, nondestructive and intuitive manner. In order to reveal the spatial-temporal scenarios of photosynthesis and yield formation regulated by circadian clock, multispectral imaging technique has been employed for nondestructive determination of circadian chlorophyll rhythms in soybean leaves. By utilizing partial least square regression analysis, the determination coefficients R2, 0.9483 for chlorophyll a and 0.8906 for chlorophyll b, were reached, respectively. The predicted chlorophyll contents extracted from multispectral data showed an approximately 24-h rhythm which could be entrained by external light conditions, consistent with the chlorophyll contents measured by chemical analyses. Visualization of chlorophyll map in each pixel offers an effective way to analyse spatial-temporal distribution of chlorophyll. Our results revealed the potentiality of multispectral imaging as a feasible nondestructive universal assay for examining clock function and robustness, as well as monitoring chlorophyll a and b and other biochemical components in plants.

  2. Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging

    PubMed Central

    Pan, Wen-Juan; Wang, Xia; Deng, Yong-Ren; Li, Jia-Hang; Chen, Wei; Chiang, John Y.; Yang, Jian-Bo; Zheng, Lei

    2015-01-01

    The circadian clock, synchronized by daily cyclic environmental cues, regulates diverse aspects of plant growth and development and increases plant fitness. Even though much is known regarding the molecular mechanism of circadian clock, it remains challenging to quantify the temporal variation of major photosynthesis products as well as their metabolic output in higher plants in a real-time, nondestructive and intuitive manner. In order to reveal the spatial-temporal scenarios of photosynthesis and yield formation regulated by circadian clock, multispectral imaging technique has been employed for nondestructive determination of circadian chlorophyll rhythms in soybean leaves. By utilizing partial least square regression analysis, the determination coefficients R2, 0.9483 for chlorophyll a and 0.8906 for chlorophyll b, were reached, respectively. The predicted chlorophyll contents extracted from multispectral data showed an approximately 24-h rhythm which could be entrained by external light conditions, consistent with the chlorophyll contents measured by chemical analyses. Visualization of chlorophyll map in each pixel offers an effective way to analyse spatial-temporal distribution of chlorophyll. Our results revealed the potentiality of multispectral imaging as a feasible nondestructive universal assay for examining clock function and robustness, as well as monitoring chlorophyll a and b and other biochemical components in plants. PMID:26059057

  3. Multi-spectral remote sensing image retrieval based on semantic extraction

    NASA Astrophysics Data System (ADS)

    Liu, Tingting; Li, Pingxiang; Zhang, Liangpei; Chen, Xu

    2008-12-01

    In this paper, a multi-level image representation model is developed and used for multi-spectral remote sensing image retrieval in order to narrow the gap between the low-level feature and high-level semantic. This model consists of an image segmentation part, a feature extraction part and semantic extraction part. The first two parts aim at the extraction of primitive region feature of an image. In these two steps, an improved JSEG algorithm is used to segment the image stored in the database, then spectral feature and texture feature are extracted for each region. In semantic extraction part, the semantic information hidden in different regions of different images is extracted by Bayesian method and expectation maximization (EM) method. At last, positive example and negative example concept is used in image retrieval instead of relevant feedback. Experiment shows that this method not only improves the accuracy of the result but also decreases the complexity of retrieval.

  4. Fast interactive registration tool for reproducible multi-spectral imaging for wound healing and treatment evaluation

    NASA Astrophysics Data System (ADS)

    Noordmans, Herke J.; de Roode, Rowland; Verdaasdonk, Rudolf

    2007-02-01

    Multi-spectral images of human tissue taken in-vivo often contain image alignment problems as patients have difficulty in retaining their posture during the acquisition time of 20 seconds. Previously, it has been attempted to correct motion errors with image registration software developed for MR or CT data but these algorithms have been proven to be too slow and erroneous for practical use with multi-spectral images. A new software package has been developed which allows the user to play a decisive role in the registration process as the user can monitor the progress of the registration continuously and force it in the right direction when it starts to fail. The software efficiently exploits videocard hardware to gain speed and to provide a perfect subvoxel correspondence between registration field and display. An 8 bit graphic card was used to efficiently register and resample 12 bit images using the hardware interpolation modes present on the graphic card. To show the feasibility of this new registration process, the software was applied in clinical practice evaluating the dosimetry for psoriasis and KTP laser treatment. The microscopic differences between images of normal skin and skin exposed to UV light proved that an affine registration step including zooming and slanting is critical for a subsequent elastic match to have success. The combination of user interactive registration software with optimal addressing the potentials of PC video card hardware greatly improves the speed of multi spectral image registration.

  5. Natural image classification in nonlinear network model

    NASA Astrophysics Data System (ADS)

    Azhar, Hanif; Iftekharuddin, Khan; Kozma, Robert; Admala, Abhinav

    2005-08-01

    We study the non-linear behavior of the KIII model for natural image classification. The KIII model is designed to be a dynamic computational model that simulates the sensory cortex. The KIII model has been explored for rudimentary pattern recognition and classification in noisy environment. We extend the study of KIII models in understanding whether self-organized neural populations can be exploited into perceptual and memory producing systems such as in natural image classification. Our goal is to obtain a quantitative index on how well the KIII model behaves when it is assigned the task to identify and distinguish one class of natural image from the other based on color and texture features. For twenty training data, twenty validation data and eighty test data set for four image classes, we obtain 80% correct classification using the KIII. We compare a standard non linear neural network tools such as back propagation for the classification of the same set of natural images and obtain 65% correct classification. We conclude that dynamic neural computational models such as KIII may be suitable candidates for natural image classification.

  6. Optoacoustic multispectral imaging of radiolucent foreign bodies in tissue.

    PubMed

    Page, Leland; Maswadi, Saher; Glickman, Randolph D

    2013-01-01

    Optoacoustic imaging is an emerging medical technology that uniquely combines the absorption contrast of optical imaging and the penetration depth of ultrasound. While it is not currently employed as a clinical imaging modality, the results of current research strongly support the use of optoacoustic-based methods in medical imaging. One such application is the diagnosis of the presence of soft tissue foreign bodies. Because many radiolucent foreign bodies have sufficient contrast for imaging in the optical domain, laser-induced optoacoustic imaging could be advantageous for the detection of such objects. Common foreign bodies have been scanned over a range of visible and near infrared wavelengths by using an optoacoustic method to obtain the spectroscopic properties of the materials commonly associated with these foreign bodies. The derived optical absorption spectra compared quite closely to the absorption spectra generated when using a conventional spectrophotometer. By using the probe-beam deflection technique, a novel, pressure-wave detection method, we successfully generated optoacoustic spectroscopic plots of a wooden foreign body embedded in a tissue phantom, which closely resembled the spectrum of the same object obtained in isolation. A practical application of such spectra is to assemble a library of spectroscopic data for radiolucent materials, from which specific characteristic wavelengths can be selected for use in optimizing imaging instrumentation and provide a basis for the identification of the material properties of particular foreign bodies.

  7. Multispectral high-resolution hologram generation using orthographic projection images

    NASA Astrophysics Data System (ADS)

    Muniraj, I.; Guo, C.; Sheridan, J. T.

    2016-08-01

    We present a new method of synthesizing a digital hologram of three-dimensional (3D) real-world objects from multiple orthographic projection images (OPI). A high-resolution multiple perspectives of 3D objects (i.e., two dimensional elemental image array) are captured under incoherent white light using synthetic aperture integral imaging (SAII) technique and their OPIs are obtained respectively. The reference beam is then multiplied with the corresponding OPI and integrated to form a Fourier hologram. Eventually, a modified phase retrieval algorithm (GS/HIO) is applied to reconstruct the hologram. The principle is validated experimentally and the results support the feasibility of the proposed method.

  8. Radiometric calibration of Landsat Thematic Mapper multispectral images

    USGS Publications Warehouse

    Chavez, P.S.

    1989-01-01

    A main problem encountered in radiometric calibration of satellite image data is correcting for atmospheric effects. Without this correction, an image digital number (DN) cannot be converted to a surface reflectance value. In this paper the accuracy of a calibration procedure, which includes a correction for atmospheric scattering, is tested. Two simple methods, a stand-alone and an in situ sky radiance measurement technique, were used to derive the HAZE DN values for each of the six reflectance Thematic Mapper (TM) bands. The DNs of two Landsat TM images of Phoenix, Arizona were converted to surface reflectances. -from Author

  9. Geometric calibration and accuracy assessment of a multispectral imager on UAVs

    NASA Astrophysics Data System (ADS)

    Zheng, Fengjie; Yu, Tao; Chen, Xingfeng; Chen, Jiping; Yuan, Guoti

    2012-11-01

    The increasing developments in Unmanned Aerial Vehicles (UAVs) platforms and associated sensing technologies have widely promoted UAVs remote sensing application. UAVs, especially low-cost UAVs, limit the sensor payload in weight and dimension. Mostly, cameras on UAVs are panoramic, fisheye lens, small-format CCD planar array camera, unknown intrinsic parameters and lens optical distortion will cause serious image aberrations, even leading a few meters or tens of meters errors in ground per pixel. However, the characteristic of high spatial resolution make accurate geolocation more critical to UAV quantitative remote sensing research. A method for MCC4-12F Multispectral Imager designed to load on UAVs has been developed and implemented. Using multi-image space resection algorithm to assess geometric calibration parameters of random position and different photogrammetric altitudes in 3D test field, which is suitable for multispectral cameras. Both theoretical and practical accuracy assessments were selected. The results of theoretical strategy, resolving object space and image point coordinate differences by space intersection, showed that object space RMSE were 0.2 and 0.14 pixels in X direction and in Y direction, image space RMSE were superior to 0.5 pixels. In order to verify the accuracy and reliability of the calibration parameters,practical study was carried out in Tianjin UAV flight experiments, the corrected accuracy validated by ground checkpoints was less than 0.3m. Typical surface reflectance retrieved on the basis of geo-rectified data was compared with ground ASD measurement resulting 4% discrepancy. Hence, the approach presented here was suitable for UAV multispectral imager.

  10. Application of High Resolution Multispectral Imagery for Levee Slide Detection and Monitoring

    NASA Technical Reports Server (NTRS)

    Hossain, A. K. M. Azad; Easson, Greg

    2007-01-01

    The objective is to develop methods to detect and monitor levee slides using commercially available high resolution multispectral imagery. High resolution multispectral imagery like IKONOS and QuickBird are suitable for detecting and monitoring levee slides. IKONOS is suitable for visual inspection, image classification and Tasseled Cap transform based slide detection. Tasseled Cap based model was found to be the best method for slide detection. QuickBird was suitable for visual inspection and image classification.

  11. Multiview matrix completion for multilabel image classification.

    PubMed

    Yong Luo; Tongliang Liu; Dacheng Tao; Chao Xu

    2015-08-01

    There is growing interest in multilabel image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion (MC) has recently been introduced as a method for transductive (semisupervised) multilabel classification, and has several distinct advantages, including robustness to missing data and background noise in both feature and label space. However, it is limited by only considering data represented by a single-view feature, which cannot precisely characterize images containing several semantic concepts. To utilize multiple features taken from different views, we have to concatenate the different features as a long vector. However, this concatenation is prone to over-fitting and often leads to very high time complexity in MC-based image classification. Therefore, we propose to weightedly combine the MC outputs of different views, and present the multiview MC (MVMC) framework for transductive multilabel image classification. To learn the view combination weights effectively, we apply a cross-validation strategy on the labeled set. In particular, MVMC splits the labeled set into two parts, and predicts the labels of one part using the known labels of the other part. The predicted labels are then used to learn the view combination coefficients. In the learning process, we adopt the average precision (AP) loss, which is particular suitable for multilabel image classification, since the ranking-based criteria are critical for evaluating a multilabel classification system. A least squares loss formulation is also presented for the sake of efficiency, and the robustness of the algorithm based on the AP loss compared with the other losses is investigated. Experimental evaluation on two real-world data sets (PASCAL VOC' 07 and MIR Flickr) demonstrate the effectiveness of MVMC for transductive (semisupervised) multilabel image classification, and show that MVMC can exploit

  12. Three-dimensional multispectral hand-held optoacoustic imaging with microsecond-level delayed laser pulses

    NASA Astrophysics Data System (ADS)

    Deán-Ben, X. L.; Bay, Erwin; Razansky, Daniel

    2015-03-01

    Three-dimensional hand-held optoacoustic imaging comes with important advantages that prompt the clinical translation of this modality, with applications envisioned in cardiovascular and peripheral vascular disease, disorders of the lymphatic system, breast cancer, arthritis or inflammation. Of particular importance is the multispectral acquisition of data by exciting the tissue at several wavelengths, which enables functional imaging applications. However, multispectral imaging of entire three-dimensional regions is significantly challenged by motion artefacts in concurrent acquisitions at different wavelengths. A method based on acquisition of volumetric datasets having a microsecond-level delay between pulses at different wavelengths is described in this work. This method can avoid image artefacts imposed by a scanning velocity greater than 2 m/s, thus, does not only facilitate imaging influenced by respiratory, cardiac or other intrinsic fast movements in living tissues, but can achieve artifact-free imaging in the presence of more significant motion, e.g., abrupt displacements during handheld-mode operation in a clinical environment.

  13. A compact snapshot multispectral imager with a monolithically integrated per-pixel filter mosaic

    NASA Astrophysics Data System (ADS)

    Geelen, Bert; Tack, Nicolaas; Lambrechts, Andy

    2014-03-01

    The adoption of spectral imaging by industry has so far been limited due to the lack of high speed, low cost and compact spectral cameras. Moreover most state-of-the-art spectral cameras utilize some form of spatial or spectral scanning during acquisition, making them ill-suited for analyzing dynamic scenes containing movement. This paper introduces a novel snapshot multispectral imager concept based on optical filters monolithically integrated on top of a standard CMOS image sensor. It overcomes the problems mentioned for scanning applications by snapshot acquisition, where an entire multispectral data cube is sensed at one discrete point in time. This is enabled by depositing interference filters per pixel directly on a CMOS image sensor, extending the traditional Bayer color imaging concept to multi- or hyperspectral imaging without a need for dedicated fore-optics. The monolithic deposition leads to a high degree of design flexibility. This enables systems ranging from application-specific, high spatial resolution cameras with 1 to 4 spectral filters, to hyperspectral snapshot cameras at medium spatial resolutions and filters laid out in cells of 4x4 to 6x6 or more. Through the use of monolithically integrated optical filters it further retains the qualities of compactness, low cost and high acquisition speed, differentiating it from other snapshot spectral cameras.

  14. Design of multi-spectral images real-time segmentation system

    NASA Astrophysics Data System (ADS)

    Zhai, Bo; Qu, Youshan; Han, Yameng; Zhou, Jiang

    2015-02-01

    In order to realize the real-time segmentation processing of multi spectral images in practice, a real-time multi-spectral images segmentation system composed of four TMS320C6455 DSPs, two Virtex-4 - V4 XC4VLX80 - FPGAs and one Virtex-2 Pro - V2 Pro20 - FPGA is designed. Through the optimization of the cooperation processing of the multi DSP and multi FPGA, the parallel multitask processing ability of the DSPs and the effective interface coordination ability of the FPGAs in the built system are used fully. In order to display the processing ability, the segmentation test experiments of 10 spectra visible images, with 1024×1024, segmented by the Multi-scale Image Segmentation Method, was done in the built multi spectral images segment system. The experiment results prove that the multi DSP and multi FPGA multi spectral images processing system designed in this paper satisfies the real-time processing requirement in engineering practice.

  15. Multispectral laser-induced fluorescence imaging system for large biological samples

    NASA Astrophysics Data System (ADS)

    Kim, Moon S.; Lefcourt, Alan M.; Chen, Yud-Ren

    2003-07-01

    A laser-induced fluorescence imaging system developed to capture multispectral fluorescence emission images simultaneously from a relatively large target object is described. With an expanded, 355-nm Nd:YAG laser as the excitation source, the system captures fluorescence emission images in the blue, green, red, and far-red regions of the spectrum centered at 450, 550, 678, and 730 nm, respectively, from a 30-cm-diameter target area in ambient light. Images of apples and of pork meat artificially contaminated with diluted animal feces have demonstrated the versatility of fluorescence imaging techniques for potential applications in food safety inspection. Regions of contamination, including sites that were not readily visible to the human eye, could easily be identified from the images.

  16. Interventional multi-spectral photoacoustic imaging in laparoscopic surgery

    NASA Astrophysics Data System (ADS)

    Hill, Emma R.; Xia, Wenfeng; Nikitichev, Daniil I.; Gurusamy, Kurinchi; Beard, Paul C.; Hawkes, David J.; Davidson, Brian R.; Desjardins, Adrien E.

    2016-03-01

    Laparoscopic procedures can be an attractive treatment option for liver resection, with a shortened hospital stay and reduced morbidity compared to open surgery. One of the central challenges of this technique is visualisation of concealed structures within the liver, particularly the vasculature and tumourous tissue. As photoacoustic (PA) imaging can provide contrast for haemoglobin in real time, it may be well suited to guiding laparoscopic procedures in order to avoid inadvertent trauma to vascular structures. In this study, a clinical laparoscopic ultrasound probe was used to receive ultrasound for PA imaging and to obtain co-registered B-mode ultrasound (US) images. Pulsed excitation light was delivered to the tissue via a fibre bundle in dark-field mode. Monte Carlo simulations were performed to optimise the light delivery geometry for imaging targets at depths of 1 cm, 2 cm and 3 cm, and 3D-printed mounts were used to position the fibre bundle relative to the transducer according to the simulation results. The performance of the photoacoustic laparoscope system was evaluated with phantoms and tissue models. The clinical potential of hybrid PA/US imaging to improve the guidance of laparoscopic surgery is discussed.

  17. Multispectral image feature fusion for detecting land mines

    SciTech Connect

    Clark, G.A.; Fields, D.J.; Sherwood, R.J.

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  18. Black phosphorus photodetector for multispectral, high-resolution imaging.

    PubMed

    Engel, Michael; Steiner, Mathias; Avouris, Phaedon

    2014-11-12

    Black phosphorus is a layered semiconductor that is intensely researched in view of applications in optoelectronics. In this letter, we investigate a multilayer black phosphorus photodetector that is capable of acquiring high-contrast (V > 0.9) images both in the visible (λVIS = 532 nm) as well as in the infrared (λIR = 1550 nm) spectral regime. In a first step, by using photocurrent microscopy, we map the active area of the device and we characterize responsivity and gain. In a second step, by deploying the black phosphorus device as a point-like detector in a confocal microsope setup, we acquire diffraction-limited optical images with submicron resolution. The results demonstrate the usefulness of black phosphorus as an optoelectronic material for hyperspectral imaging applications.

  19. Correction of motion artefacts and pseudo colour visualization of multispectral light scattering images for optical diagnosis of rheumatoid arthritis

    NASA Astrophysics Data System (ADS)

    Minet, Olaf; Scheibe, Patrick; Beuthan, Jürgen; Zabarylo, Urszula

    2010-02-01

    State-of-the-art image processing methods offer new possibilities for diagnosing diseases using scattered light. The optical diagnosis of rheumatism is taken as an example to show that the diagnostic sensitivity can be improved using overlapped pseudo-coloured images of different wavelengths, provided that multispectral images are recorded to compensate for any motion related artefacts which occur during examination.

  20. Correction of motion artefacts and pseudo colour visualization of multispectral light scattering images for optical diagnosis of rheumatoid arthritis

    NASA Astrophysics Data System (ADS)

    Minet, Olaf; Scheibe, Patrick; Beuthan, Jürgen; Zabarylo, Urszula

    2009-10-01

    State-of-the-art image processing methods offer new possibilities for diagnosing diseases using scattered light. The optical diagnosis of rheumatism is taken as an example to show that the diagnostic sensitivity can be improved using overlapped pseudo-coloured images of different wavelengths, provided that multispectral images are recorded to compensate for any motion related artefacts which occur during examination.

  1. Using Non-Invasive Multi-Spectral Imaging to Quantitatively Assess Tissue Vasculature

    SciTech Connect

    Vogel, A; Chernomordik, V; Riley, J; Hassan, M; Amyot, F; Dasgeb, B; Demos, S G; Pursley, R; Little, R; Yarchoan, R; Tao, Y; Gandjbakhche, A H

    2007-10-04

    This research describes a non-invasive, non-contact method used to quantitatively analyze the functional characteristics of tissue. Multi-spectral images collected at several near-infrared wavelengths are input into a mathematical optical skin model that considers the contributions from different analytes in the epidermis and dermis skin layers. Through a reconstruction algorithm, we can quantify the percent of blood in a given area of tissue and the fraction of that blood that is oxygenated. Imaging normal tissue confirms previously reported values for the percent of blood in tissue and the percent of blood that is oxygenated in tissue and surrounding vasculature, for the normal state and when ischemia is induced. This methodology has been applied to assess vascular Kaposi's sarcoma lesions and the surrounding tissue before and during experimental therapies. The multi-spectral imaging technique has been combined with laser Doppler imaging to gain additional information. Results indicate that these techniques are able to provide quantitative and functional information about tissue changes during experimental drug therapy and investigate progression of disease before changes are visibly apparent, suggesting a potential for them to be used as complementary imaging techniques to clinical assessment.

  2. Rapid algal culture diagnostics for open ponds using multispectral image analysis.

    PubMed

    Murphy, Thomas E; Macon, Keith; Berberoglu, Halil

    2014-01-01

    This article presents a multispectral image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems.

  3. Rapid algal culture diagnostics for open ponds using multispectral image analysis.

    PubMed

    Murphy, Thomas E; Macon, Keith; Berberoglu, Halil

    2014-01-01

    This article presents a multispectral image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems. PMID:24265121

  4. A mixture neural net for multispectral imaging spectrometer processing

    NASA Technical Reports Server (NTRS)

    Casasent, David; Slagle, Timothy

    1990-01-01

    Each spatial region viewed by an imaging spectrometer contains various elements in a mixture. The elements present and the amount of each are to be determined. A neural net solution is considered. Initial optical neural net hardware is described. The first simulations on the component requirements of a neural net are considered. The pseudoinverse solution is shown to not suffice, i.e. a neural net solution is required.

  5. Diagnosis of cutaneous thermal burn injuries by multispectral imaging analysis

    NASA Technical Reports Server (NTRS)

    Anselmo, V. J.; Zawacki, B. E.

    1978-01-01

    Special photographic or television image analysis is shown to be a potentially useful technique to assist the physician in the early diagnosis of thermal burn injury. A background on the medical and physiological problems of burns is presented. The proposed methodology for burns diagnosis from both the theoretical and clinical points of view is discussed. The television/computer system constructed to accomplish this analysis is described, and the clinical results are discussed.

  6. Science applications of a multispectral microscopic imager for the astrobiological exploration of Mars.

    PubMed

    Núñez, Jorge I; Farmer, Jack D; Sellar, R Glenn; Swayze, Gregg A; Blaney, Diana L

    2014-02-01

    Future astrobiological missions to Mars are likely to emphasize the use of rovers with in situ petrologic capabilities for selecting the best samples at a site for in situ analysis with onboard lab instruments or for caching for potential return to Earth. Such observations are central to an understanding of the potential for past habitable conditions at a site and for identifying samples most likely to harbor fossil biosignatures. The Multispectral Microscopic Imager (MMI) provides multispectral reflectance images of geological samples at the microscale, where each image pixel is composed of a visible/shortwave infrared spectrum ranging from 0.46 to 1.73 μm. This spectral range enables the discrimination of a wide variety of rock-forming minerals, especially Fe-bearing phases, and the detection of hydrated minerals. The MMI advances beyond the capabilities of current microimagers on Mars by extending the spectral range into the infrared and increasing the number of spectral bands. The design employs multispectral light-emitting diodes and an uncooled indium gallium arsenide focal plane array to achieve a very low mass and high reliability. To better understand and demonstrate the capabilities of the MMI for future surface missions to Mars, we analyzed samples from Mars-relevant analog environments with the MMI. Results indicate that the MMI images faithfully resolve the fine-scale microtextural features of samples and provide important information to help constrain mineral composition. The use of spectral endmember mapping reveals the distribution of Fe-bearing minerals (including silicates and oxides) with high fidelity, along with the presence of hydrated minerals. MMI-based petrogenetic interpretations compare favorably with laboratory-based analyses, revealing the value of the MMI for future in situ rover-mediated astrobiological exploration of Mars.

  7. Spectral ladar: towards active 3D multispectral imaging

    NASA Astrophysics Data System (ADS)

    Powers, Michael A.; Davis, Christopher C.

    2010-04-01

    In this paper we present our Spectral LADAR concept, an augmented implementation of traditional LADAR. This sensor uses a polychromatic source to obtain range-resolved 3D spectral images which are used to identify objects based on combined spatial and spectral features, resolving positions in three dimensions and up to hundreds of meters in distance. We report on a proof-of-concept Spectral LADAR demonstrator that generates spectral point clouds from static scenes. The demonstrator transmits nanosecond supercontinuum pulses generated in a photonic crystal fiber. Currently we use a rapidly tuned receiver with a high-speed InGaAs APD for 25 spectral bands with the future expectation of implementing a linear APD array spectrograph. Each spectral band is independently range resolved with multiple return pulse recognition. This is a critical feature, enabling simultaneous spectral and spatial unmixing of partially obscured objects when not achievable using image fusion of monochromatic LADAR and passive spectral imagers. This enables higher identification confidence in highly cluttered environments such as forested or urban areas (e.g. vehicles behind camouflage or foliage). These environments present challenges for situational awareness and robotic perception which can benefit from the unique attributes of Spectral LADAR. Results from this demonstrator unit are presented for scenes typical of military operations and characterize the operation of the device. The results are discussed here in the context of autonomous vehicle navigation and target recognition.

  8. Snap-shot multispectral imaging of vascular dynamics in a mouse window-chamber model.

    PubMed

    Hendargo, Hansford C; Zhao, Yulin; Allenby, Taylor; Palmer, Gregory M

    2015-07-15

    Understanding tumor vascular dynamics through parameters such as blood flow and oxygenation can yield insight into tumor biology and therapeutic response. Hyperspectral microscopy enables optical detection of hemoglobin saturation or blood velocity by either acquiring multiple images that are spectrally distinct or by rapid acquisition at a single wavelength over time. However, the serial acquisition of spectral images over time prevents the ability to monitor rapid changes in vascular dynamics and cannot monitor concurrent changes in oxygenation and flow rate. Here, we introduce snap shot-multispectral imaging (SS-MSI) for use in imaging the microvasculature in mouse dorsal-window chambers. By spatially multiplexing spectral information into a single-image capture, simultaneous acquisition of dynamic hemoglobin saturation and blood flow over time is achieved down to the capillary level and provides an improved optical tool for monitoring rapid in vivo vascular dynamics. PMID:26176452

  9. Multispectral imaging approach for simplified non-invasive in-vivo evaluation of gingival erythema

    NASA Astrophysics Data System (ADS)

    Eckhard, Timo; Valero, Eva M.; Nieves, Juan L.; Gallegos-Rueda, José M.; Mesa, Francisco

    2012-03-01

    Erythema is a common visual sign of gingivitis. In this work, a new and simple low-cost image capture and analysis method for erythema assessment is proposed. The method is based on digital still images of gingivae and applied on a pixel-by-pixel basis. Multispectral images are acquired with a conventional digital camera and multiplexed LED illumination panels at 460nm and 630nm peak wavelength. An automatic work-flow segments teeth from gingiva regions in the images and creates a map of local blood oxygenation levels, which relates to the presence of erythema. The map is computed from the ratio of the two spectral images. An advantage of the proposed approach is that the whole process is easy to manage by dental health care professionals in clinical environment.

  10. Interventional multispectral photoacoustic imaging with a clinical linear array ultrasound probe for guiding nerve blocks

    NASA Astrophysics Data System (ADS)

    Xia, Wenfeng; West, Simeon J.; Nikitichev, Daniil I.; Ourselin, Sebastien; Beard, Paul C.; Desjardins, Adrien E.

    2016-03-01

    Accurate identification of tissue structures such as nerves and blood vessels is critically important for interventional procedures such as nerve blocks. Ultrasound imaging is widely used as a guidance modality to visualize anatomical structures in real-time. However, identification of nerves and small blood vessels can be very challenging, and accidental intra-neural or intra-vascular injections can result in significant complications. Multi-spectral photoacoustic imaging can provide high sensitivity and specificity for discriminating hemoglobin- and lipid-rich tissues. However, conventional surface-illumination-based photoacoustic systems suffer from limited sensitivity at large depths. In this study, for the first time, an interventional multispectral photoacoustic imaging (IMPA) system was used to image nerves in a swine model in vivo. Pulsed excitation light with wavelengths in the ranges of 750 - 900 nm and 1150 - 1300 nm was delivered inside the body through an optical fiber positioned within the cannula of an injection needle. Ultrasound waves were received at the tissue surface using a clinical linear array imaging probe. Co-registered B-mode ultrasound images were acquired using the same imaging probe. Nerve identification was performed using a combination of B-mode ultrasound imaging and electrical stimulation. Using a linear model, spectral-unmixing of the photoacoustic data was performed to provide image contrast for oxygenated and de-oxygenated hemoglobin, water and lipids. Good correspondence between a known nerve location and a lipid-rich region in the photoacoustic images was observed. The results indicate that IMPA is a promising modality for guiding nerve blocks and other interventional procedures. Challenges involved with clinical translation are discussed.

  11. 3D Multispectral Light Propagation Model For Subcutaneous Veins Imaging

    SciTech Connect

    Paquit, Vincent C; Price, Jeffery R; Meriaudeau, Fabrice; Tobin Jr, Kenneth William

    2008-01-01

    In this paper, we describe a new 3D light propagation model aimed at understanding the effects of various physiological properties on subcutaneous vein imaging. In particular, we build upon the well known MCML (Monte Carlo Multi Layer) code and present a tissue model that improves upon the current state-of-the-art by: incorporating physiological variation, such as melanin concentration, fat content, and layer thickness; including veins of varying depth and diameter; using curved surfaces from real arm shapes; and modeling the vessel wall interface. We describe our model, present results from the Monte Carlo modeling, and compare these results with those obtained with other Monte Carlo methods.

  12. Multispectral X-ray imaging with a multichannel Kirkpatrick-Baez microscope for imploded core temperature observation

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Fang, Zhi-Heng; Jia, Guo; Yi, Sheng-Zhen; Tu, Yu-Chun; Zhu, Jing-Tao; Mu, Bao-Zhong; An, Hong-Hai; Wang, Rui-Rong; Xie, Zhi-Yong; Ye, Jun-Jian; Meng, Xiang-Fu; Zhou, Hua-Zhen; Wang, Chen; Lei, An-Le; Wang, Zhan-Shan; Fu, Si-Zu

    2014-05-01

    The coupling efficiency of short-pulse ignition laser energy to hot-spot internal energy directly affects the feasibility of fast ignition. Experimental characterization of the hot spot has attracted much attention. Among temperature, density and neutron yield of fast ignition experiments, the temperature of the hot spot has few available diagnostic methods. Multispectral X-ray imaging of hot-spot continuum emission is expected to give the time evolution of the electron temperature distribution. This article describes electron temperature determination from multispectral imaging, a dual-channel X-ray Kirkpatrick-Baez (KB) microscope designed for two-spectral imaging, and the experimental results of hot-core multispectral imaging of an imploded cone-shell target at the SG-II laser facility.

  13. Design of a multi-spectral imager built using the compressive sensing single-pixel camera architecture

    NASA Astrophysics Data System (ADS)

    McMackin, Lenore; Herman, Matthew A.; Weston, Tyler

    2016-02-01

    We present the design of a multi-spectral imager built using the architecture of the single-pixel camera. The architecture is enabled by the novel sampling theory of compressive sensing implemented optically using the Texas Instruments DLP™ micro-mirror array. The array not only implements spatial modulation necessary for compressive imaging but also provides unique diffractive spectral features that result in a multi-spectral, high-spatial resolution imager design. The new camera design provides multi-spectral imagery in a wavelength range that extends from the visible to the shortwave infrared without reduction in spatial resolution. In addition to the compressive imaging spectrometer design, we present a diffractive model of the architecture that allows us to predict a variety of detailed functional spatial and spectral design features. We present modeling results, architectural design and experimental results that prove the concept.

  14. Soft nanomaterial-based targeting polymersomes for near-infrared fluorescence multispectral in vivo imaging.

    PubMed

    Li, Zuhong; Wu, Liyuan; Hu, Peiran; Han, Sihai; Zhang, Tao; Fan, Hongliang; Jin, Wei; Jin, Qinhan; Mu, Ying

    2012-11-21

    We report here the soft nanomaterial-based targeting polymersomes for near-infrared (NIR) fluorescence imaging to carry out in vivo tumor detection. Two polymersome-based NIR fluorescent probes were prepared through the self-assembly of amphiphilic block copolymers, poly(butadiene-b-ethylene oxide) (PEO-b-PBD). Each of them was encapsulated with distinct hydrophobic near-infrared dyes (DiD and DiR) and modified with different targeting ligands (anti-CEA antibody and anti-EGFR antibody), respectively. After simultaneous injection of these two probes into the tumor-bearing mice via tail vein, multispectral near-infrared fluorescence images were obtained. The results indicate that both probes are successfully directed to the tumor foci, where two distinguishable fluorescent signals were detected through the unmixed fluorescence images. By taking advantage of two targeting polymersome-based probes with distinct fluorescent features, the proposed multispectral near-infrared fluorescence imaging method can greatly improve the specificity and accuracy for in vivo tumor detection.

  15. Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping.

    PubMed

    Chang, Yi; Yan, Luxin; Fang, Houzhang; Luo, Chunan

    2015-06-01

    Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner. We propose to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral-spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension. As a result, a more comprehensive stripes and random noise are perfectly removed, while the edges and detail information are well preserved. In addition, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. We extensively validate our method under various stripe categories and show comparison with other approaches with respect to result quality, running time, and quantitative assessments.

  16. Developing handheld real time multispectral imager to clinically detect erythema in darkly pigmented skin

    NASA Astrophysics Data System (ADS)

    Kong, Linghua; Sprigle, Stephen; Yi, Dingrong; Wang, Fengtao; Wang, Chao; Liu, Fuhan

    2010-02-01

    Pressure ulcers have been identified as a public health concern by the US government through the Healthy People 2010 initiative and the National Quality Forum (NQF). Currently, no tools are available to assist clinicians in erythema, i.e. the early stage pressure ulcer detection. The results from our previous research (supported by NIH grant) indicate that erythema in different skin tones can be identified using a set of wavelengths 540, 577, 650 and 970nm. This paper will report our recent work which is developing a handheld, point-of-care, clinicallyviable and affordable, real time multispectral imager to detect erythema in persons with darkly pigmented skin. Instead of using traditional filters, e.g. filter wheels, generalized Lyot filter, electrical tunable filter or the methods of dispersing light, e.g. optic-acoustic crystal, a novel custom filter mosaic has been successfully designed and fabricated using lithography and vacuum multi layer film technologies. The filter has been integrated with CMOS and CCD sensors. The filter incorporates four or more different wavelengths within the visual to nearinfrared range each having a narrow bandwidth of 30nm or less. Single wavelength area is chosen as 20.8μx 20.8μ. The filter can be deposited on regular optical glass as substrate or directly on a CMOS and CCD imaging sensor. This design permits a multi-spectral image to be acquired in a single exposure, thereby providing overwhelming convenience in multi spectral imaging acquisition.

  17. Soft nanomaterial-based targeting polymersomes for near-infrared fluorescence multispectral in vivo imaging

    NASA Astrophysics Data System (ADS)

    Li, Zuhong; Wu, Liyuan; Hu, Peiran; Han, Sihai; Zhang, Tao; Fan, Hongliang; Jin, Wei; Jin, Qinhan; Mu, Ying

    2012-10-01

    We report here the soft nanomaterial-based targeting polymersomes for near-infrared (NIR) fluorescence imaging to carry out in vivo tumor detection. Two polymersome-based NIR fluorescent probes were prepared through the self-assembly of amphiphilic block copolymers, poly(butadiene-b-ethylene oxide) (PEO-b-PBD). Each of them was encapsulated with distinct hydrophobic near-infrared dyes (DiD and DiR) and modified with different targeting ligands (anti-CEA antibody and anti-EGFR antibody), respectively. After simultaneous injection of these two probes into the tumor-bearing mice via tail vein, multispectral near-infrared fluorescence images were obtained. The results indicate that both probes are successfully directed to the tumor foci, where two distinguishable fluorescent signals were detected through the unmixed fluorescence images. By taking advantage of two targeting polymersome-based probes with distinct fluorescent features, the proposed multispectral near-infrared fluorescence imaging method can greatly improve the specificity and accuracy for in vivo tumor detection.

  18. Multispectral image enhancement for H&E stained pathological tissue specimens

    NASA Astrophysics Data System (ADS)

    Bautista, Pinky A.; Abe, Tokiya; Yamaguchi, Masahiro; Ohyama, Nagaaki; Yagi, Yukako

    2008-03-01

    The presence of a liver disease such as cirrhosis can be determined by examining the proliferation of collagen fiber from a tissue slide stained with special stain such as the Masson's trichrome(MT) stain. Collagen fiber and smooth muscle, which are both stained the same in an H&E stained slide, are stained blue and pink respectively in an MT-stained slide. In this paper we show that with multispectral imaging the difference between collagen fiber and smooth muscle can be visualized even from an H&E stained image. In the method M KL bases are derived using the spectral data of those H&E stained tissue components which can be easily differentiated from each other, i.e. nucleus, cytoplasm, red blood cells, etc. and based on the spectral residual error of fiber weighting factors are determined to enhance spectral features at certain wavelengths. Results of our experiment demonstrate the capability of multispectral imaging and its advantage compared to the conventional RGB imaging systems to delineate tissue structures with subtle colorimetric difference.

  19. Multi-Spectral imaging of vegetation for detecting CO2 leaking from underground

    SciTech Connect

    Rouse, J.H.; Shaw, J.A.; Lawrence, R.L.; Lewicki, J.L.; Dobeck, L.M.; Repasky, K.S.; Spangler, L.H.

    2010-06-01

    Practical geologic CO{sub 2} sequestration will require long-term monitoring for detection of possible leakage back into the atmosphere. One potential monitoring method is multi-spectral imaging of vegetation reflectance to detect leakage through CO{sub 2}-induced plant stress. A multi-spectral imaging system was used to simultaneously record green, red, and near-infrared (NIR) images with a real-time reflectance calibration from a 3-m tall platform, viewing vegetation near shallow subsurface CO{sub 2} releases during summers 2007 and 2008 at the Zero Emissions Research and Technology field site in Bozeman, Montana. Regression analysis of the band reflectances and the Normalized Difference Vegetation Index with time shows significant correlation with distance from the CO{sub 2} well, indicating the viability of this method to monitor for CO{sub 2} leakage. The 2007 data show rapid plant vigor degradation at high CO{sub 2} levels next to the well and slight nourishment at lower, but above-background CO{sub 2} concentrations. Results from the second year also show that the stress response of vegetation is strongly linked to the CO{sub 2} sink-source relationship and vegetation density. The data also show short-term effects of rain and hail. The real-time calibrated imaging system successfully obtained data in an autonomous mode during all sky and daytime illumination conditions.

  20. Spectral and multispectral imaging studies of lunar mantled mare deposits

    NASA Technical Reports Server (NTRS)

    Blewett, D. T.; Hawke, B. R.; Lucey, P. G.; Bell, J. F., III; Jaumann, R.; Hiesinger, H.; Neukum, G.; Spudis, P. D.

    1993-01-01

    Near-IR reflectance spectra (0.6-2.5 microns) and CCD images in the extended visible range (0.4-1.0 microns) obtained with Earth-based telescopes have been used to investigate the composition and origin of formations in the Schiller-Schickard region of the Moon. Of particular interest are the Schickard light plains, which represent an area of mantled mare basalt, or cryptomare. Here local pre-existing mare basalts were eroded and incorporated into a highlands-rich deposit by eject a from the Orientale Basin. Spectra observations of mature and immature highland and mare surfaces, as well as dark-halo crater materials provide information on the mafic mineralogy of features in the area. Analyses of the '1 micron' absorption band and spectral mixing models indicate that selected spots in the light plains contain on the order of 50 percent mare basalt. CCD image cubes can be used to map the amount of basalt in the light plains and evaluate changes with radial distance from Orientale.

  1. Portable multispectral fluorescence imaging system for food safety applications

    NASA Astrophysics Data System (ADS)

    Lefcourt, Alan M.; Kim, Moon S.; Chen, Yud-Ren

    2004-03-01

    Fluorescence can be a sensitive method for detecting food contaminants. Of particular interest is detection of fecal contamination as feces is the source of many pathogenic organisms. Feces generally contain chlorophyll a and related compounds due to ingestion of plant materials, and these compounds can readily be detected using fluorescence techniques. Described is a fluorescence-imaging system consisting primarily of a UV light source, an intensified camera with a six-position filter wheel, and software for controlling the system and automatically analyzing the resulting images. To validate the system, orchard apples artificially contaminated with dairy feces were used in a "hands-on" public demonstration. The contamination sites were easily identified using automated edge detection and threshold detection algorithms. In addition, by applying feces to apples and then washing sets of apples at hourly intervals, it was determined that five h was the minimum contact time that allowed identification of the contamination site after the apples were washed. There are many potential uses for this system, including studying the efficacy of apple washing systems.

  2. Estimating Scots Pine Tree Mortality Using High Resolution Multispectral Images

    NASA Astrophysics Data System (ADS)

    Buriak, L.; Sukhinin, A. I.; Conard, S. G.; Ivanova, G. A.; McRae, D. J.; Soja, A. J.; Okhotkina, E.

    2010-12-01

    Scots pine (Pinus sylvestris) forest stands of central Siberia are characterized by a mixed-severity fire regime that is dominated by low- to high-severity surface fires, with crown fires occurring less frequently. The purpose of this study was to link ground measurements with air-borne and satellite observations of active wildfires and older fire scars to better estimate tree mortality remotely. Data from field sampling on experimental fires and wildfires were linked with intermediate-resolution satellite (Landsat Enhanced Thematic Mapper) data to estimate fire severity and carbon emissions. Results are being applied to Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, MERIS, Landsat-ETM, SPOT (i.e., low, middle and high spatial resolution), to understand their remote-sensing capability for mapping fire severity, as indicated by tree mortality. Tree mortality depends on fireline intensity, residence time, and the physiological effects on the cambium layer, foliage and roots. We have correlated tree mortality measured after fires of varying severity with NDVI and other Chlorophyll Indexes to model tree mortality on a landscape scale. The field data obtained on experimental and wildfires are being analyzed and compared with intermediate-resolution satellite data (Landsat7-ETM) to help estimate fire severity, emissions, and carbon balance. In addition, it is being used to monitor immediate ecosystem fire effects (e.g., tree mortality) and long-term postfire vegetation recovery. These data are also being used to validate AVHRR , MODIS, and MERIS estimates of burn area. We studied burned areas in the Angara Region of central Siberia (northeast of Lake Baikal) for which both ground data and satellite data (ENVISAT-MERIS, Spot4, Landsat5, Landsat7-ETM) were available for the 2003 - 2004 and 2006 - 2008 periods. Ground validation was conducted on seventy sample plots established on burned sites differing in

  3. [Nondestructive detection of grey mold of eggplant based on ground multi-spectral imaging sensor].

    PubMed

    Wu, Di; Zhu, Deng-Sheng; He, Yong; Zhang, Chuan-Qing; Feng, Lei

    2008-07-01

    Botrytis cinerea Pers. is a worldwide fungus. It is a severe threat to eggplant. Chemistry methods can do an accurate identification, however they are time-consuming, require execution by professionals and are high cost. The present paper presents the development of a ground based multi-spectral imaging sensor for the grey mold detection. Three channels (green, red, near-infrared) of crop images were acquired. Two algorithm systems were developed. The objective of the image processing is to obtain a binary image, which could point out the location of symptoms as accurately as possible. Two image processing methods were developed. It could be seen that method 1 can diagnose the symptoms accurately even if the symptoms are small while method 2 can only diagnose the symptoms with a certain extent area and the detection of symptoms is not very accurate. However, the images processed by method 1 showed some error diagnoses while the method 2 did not. It was concluded that both methods have some advantages and disadvantages. In the agriculture practice, the diagnosis environment will be more complex than in the greenhouse. Some things such as dry soil and perished leaf fragment will disturb the symptom detection by naked eyes when the grower stands away from the leaf. Two image process methods can diagnose the symptoms clearly although the position based on method 2 was a little deviated. It was concluded that the symptoms were well detected using multi-spectra imaging technique even there were some disturbances. Thus, multi-spectral imaging technique is available for the symptoms detection of grey mold on eggplant leaves.

  4. Design and evaluation of a device for fast multispectral time-resolved fluorescence spectroscopy and imaging

    PubMed Central

    Yankelevich, Diego R.; Ma, Dinglong; Liu, Jing; Sun, Yang; Sun, Yinghua; Bec, Julien; Elson, Daniel S.; Marcu, Laura

    2014-01-01

    The application of time-resolved fluorescence spectroscopy (TRFS) to in vivo tissue diagnosis requires a method for fast acquisition of fluorescence decay profiles in multiple spectral bands. This study focusses on development of a clinically compatible fiber-optic based multispectral TRFS (ms-TRFS) system together with validation of its accuracy and precision for fluorescence lifetime measurements. It also presents the expansion of this technique into an imaging spectroscopy method. A tandem array of dichroic beamsplitters and filters was used to record TRFS decay profiles at four distinct spectral bands where biological tissue typically presents fluorescence emission maxima, namely, 390, 452, 542, and 629 nm. Each emission channel was temporally separated by using transmission delays through 200 μm diameter multimode optical fibers of 1, 10, 19, and 28 m lengths. A Laguerre-expansion deconvolution algorithm was used to compensate for modal dispersion inherent to large diameter optical fibers and the finite bandwidth of detectors and digitizers. The system was found to be highly efficient and fast requiring a few nano-Joule of laser pulse energy and <1 ms per point measurement, respectively, for the detection of tissue autofluorescent components. Organic and biological chromophores with lifetimes that spanned a 0.8–7 ns range were used for system validation, and the measured lifetimes from the organic fluorophores deviated by less than 10% from values reported in the literature. Multi-spectral lifetime images of organic dye solutions contained in glass capillary tubes were recorded by raster scanning the single fiber probe in a 2D plane to validate the system as an imaging tool. The lifetime measurement variability was measured indicating that the system provides reproducible results with a standard deviation smaller than 50 ps. The ms-TRFS is a compact apparatus that makes possible the fast, accurate, and precise multispectral time-resolved fluorescence

  5. Design and evaluation of a device for fast multispectral time-resolved fluorescence spectroscopy and imaging.

    PubMed

    Yankelevich, Diego R; Ma, Dinglong; Liu, Jing; Sun, Yang; Sun, Yinghua; Bec, Julien; Elson, Daniel S; Marcu, Laura

    2014-03-01

    The application of time-resolved fluorescence spectroscopy (TRFS) to in vivo tissue diagnosis requires a method for fast acquisition of fluorescence decay profiles in multiple spectral bands. This study focusses on development of a clinically compatible fiber-optic based multispectral TRFS (ms-TRFS) system together with validation of its accuracy and precision for fluorescence lifetime measurements. It also presents the expansion of this technique into an imaging spectroscopy method. A tandem array of dichroic beamsplitters and filters was used to record TRFS decay profiles at four distinct spectral bands where biological tissue typically presents fluorescence emission maxima, namely, 390, 452, 542, and 629 nm. Each emission channel was temporally separated by using transmission delays through 200 μm diameter multimode optical fibers of 1, 10, 19, and 28 m lengths. A Laguerre-expansion deconvolution algorithm was used to compensate for modal dispersion inherent to large diameter optical fibers and the finite bandwidth of detectors and digitizers. The system was found to be highly efficient and fast requiring a few nano-Joule of laser pulse energy and <1 ms per point measurement, respectively, for the detection of tissue autofluorescent components. Organic and biological chromophores with lifetimes that spanned a 0.8-7 ns range were used for system validation, and the measured lifetimes from the organic fluorophores deviated by less than 10% from values reported in the literature. Multi-spectral lifetime images of organic dye solutions contained in glass capillary tubes were recorded by raster scanning the single fiber probe in a 2D plane to validate the system as an imaging tool. The lifetime measurement variability was measured indicating that the system provides reproducible results with a standard deviation smaller than 50 ps. The ms-TRFS is a compact apparatus that makes possible the fast, accurate, and precise multispectral time-resolved fluorescence lifetime

  6. Design and evaluation of a device for fast multispectral time-resolved fluorescence spectroscopy and imaging

    SciTech Connect

    Yankelevich, Diego R.; Ma, Dinglong; Liu, Jing; Sun, Yang; Sun, Yinghua; Bec, Julien; Marcu, Laura; Elson, Daniel S.

    2014-03-15

    The application of time-resolved fluorescence spectroscopy (TRFS) to in vivo tissue diagnosis requires a method for fast acquisition of fluorescence decay profiles in multiple spectral bands. This study focusses on development of a clinically compatible fiber-optic based multispectral TRFS (ms-TRFS) system together with validation of its accuracy and precision for fluorescence lifetime measurements. It also presents the expansion of this technique into an imaging spectroscopy method. A tandem array of dichroic beamsplitters and filters was used to record TRFS decay profiles at four distinct spectral bands where biological tissue typically presents fluorescence emission maxima, namely, 390, 452, 542, and 629 nm. Each emission channel was temporally separated by using transmission delays through 200 μm diameter multimode optical fibers of 1, 10, 19, and 28 m lengths. A Laguerre-expansion deconvolution algorithm was used to compensate for modal dispersion inherent to large diameter optical fibers and the finite bandwidth of detectors and digitizers. The system was found to be highly efficient and fast requiring a few nano-Joule of laser pulse energy and <1 ms per point measurement, respectively, for the detection of tissue autofluorescent components. Organic and biological chromophores with lifetimes that spanned a 0.8–7 ns range were used for system validation, and the measured lifetimes from the organic fluorophores deviated by less than 10% from values reported in the literature. Multi-spectral lifetime images of organic dye solutions contained in glass capillary tubes were recorded by raster scanning the single fiber probe in a 2D plane to validate the system as an imaging tool. The lifetime measurement variability was measured indicating that the system provides reproducible results with a standard deviation smaller than 50 ps. The ms-TRFS is a compact apparatus that makes possible the fast, accurate, and precise multispectral time-resolved fluorescence

  7. An interactive lake survey program. [airborne multispectral sensor image processing

    NASA Technical Reports Server (NTRS)

    Smith, A. Y.

    1977-01-01

    Consideration is given to the development and operation of the interactive lake survey program developed by the Jet Propulsion Laboratory and the Environmental Protection Agency. The program makes it possible to locate, isolate, and store any number of water bodies on the basis of a given digital image. The stored information may be used to generate statistical analyses of each body of water including the lake surface area and the shoreline perimeter. The hardware includes a 360/65 host computer, a Ramtek G100B display controller, and a trackball cursor. The system is illustrated by the LAKELOC operation as it would be applied to a Landsat scene, noting the FARINA and STATUS programs. The water detection algorithm, which increases the accuracy with which water and land data may be separated, is discussed.

  8. Cloud classification using whole-sky imager data

    SciTech Connect

    Buch, K.A. Jr.; Sun, C.H.; Thorne, L.R.

    1996-04-01

    Clouds are one of the most important moderators of the earth radiation budget and one of the least understood. The effect that clouds have on the reflection and absorption of solar and terrestrial radiation is strongly influenced by their shape, size, and composition. Physically accurate parameterization of clouds is necessary for any general circulation model (GCM) to yield meaningful results. The work presented here is part of a larger project that is aimed at producing realistic three-dimensional (3D) volume renderings of cloud scenes based on measured data from real cloud scenes. These renderings will provide the important shape information for parameterizing GCMs. The specific goal of the current study is to develop an algorithm that automatically classifies (by cloud type) the clouds observed in the scene. This information will assist the volume rendering program in determining the shape of the cloud. Much work has been done on cloud classification using multispectral satellite images. Most of these references use some kind of texture measure to distinguish the different cloud types and some also use topological features (such as cloud/sky connectivity or total number of clouds). A wide variety of classification methods has been used, including neural networks, various types of clustering, and thresholding. The work presented here uses binary decision trees to distinguish the different cloud types based on cloud features vectors.

  9. A neural network approach for enhancing information extraction from multispectral image data

    USGS Publications Warehouse

    Liu, J.; Shao, G.; Zhu, H.; Liu, S.

    2005-01-01

    A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

  10. [Study on application of multi-spectral image texture to discriminating rice categories based on wavelet packet and support vector machine].

    PubMed

    Chen, Xiao-Jing; Wu, Di; He, Yong; Liu, Shou

    2009-01-01

    Based on multi-spectral digital image texture feature, a new rapid and nondestructive method for discriminating rice categories was put forward. The new method combined the advantages of wavelet packet and support vector machine (SVM). In the present study, the images which are 1 036 pixels in vertical direction by 1,384 pixels in horizontal direction with 24-bit depth were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multi-spectral digital imager. The three wavebands of image (red, green and NIR) can be composed into one image which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. NIR waveband images were decomposed to 16 subbands using three wavelet packet multi-resolution. Because the main feature of texture information is concentrated on the middle frequency, the 8 subbands of middle frequency were selected to calculate entropy, and the entropy of three wavebands of original image was calculated at the same time. Eighty images (twenty for each category) were used for calibration set and eighty images (twenty for each category) were used as the prediction set. Then the rice categories were classified by SVM. The classification rate of rice categories was only 93.75% using the entropy of original image, but reached 100% by wavelet packet decomposition. The overall results show that the technique combining wavelet packet and support vector machine can be efficiently utilized for texture recognition of multi-spectra, and is an effective and simple technique for discriminating the rice categories. This study also provides a foundation for rice grading and other rice industry processing such as quality diction and milling degree.

  11. Hurricane coastal flood analysis using multispectral spectral images

    NASA Astrophysics Data System (ADS)

    Ogashawara, I.; Ferreira, C.; Curtarelli, M. P.

    2013-12-01

    Flooding is one of the main hazards caused by extreme events such as hurricanes and tropical storms. Therefore, flood maps are a crucial tool to support policy makers, environmental managers and other government agencies for emergency management, disaster recovery and risk reduction planning. However traditional flood mapping methods rely heavily on the interpolation of hydrodynamic models results, and most recently, the extensive collection of field data. These methods are time-consuming, labor intensive, and costly. Efficient and fast response alternative methods should be developed in order to improve flood mapping, and remote sensing has been proved as a valuable tool for this application. Our goal in this paper is to introduce a novel technique based on spectral analysis in order to aggregate knowledge and information to map coastal flood areas. For this purpose we used the Normalized Diference Water Index (NDWI) which was derived from two the medium resolution LANDSAT/TM 5 surface reflectance product from the LANDSAT climate data record (CDR). This product is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). We used the surface reflectance products acquired before and after the passage of Hurricane Ike for East Texas in September of 2008. We used as end member a classification of estimated flooded area based on the United States Geological Survey (USGS) mobile storm surge network that was deployed for Hurricane Ike. We used a dataset which consisted of 59 water levels recording stations. The estimated flooded area was delineated interpolating the maximum surge in each location using a spline with barriers method with high tension and a 30 meter Digital Elevation Model (DEM) from the National Elevation Dataset (NED). Our results showed that, in the flooded area, the NDWI values decreased after the hurricane landfall on average from 0.38 to 0.18 and the median value decreased from 0.36 to 0.2. However

  12. A polarized multispectral imaging system for quantitative assessment of hypertrophic scars

    PubMed Central

    Ghassemi, Pejhman; Travis, Taryn E.; Moffatt, Lauren T.; Shupp, Jeffrey W.; Ramella-Roman, Jessica C.

    2014-01-01

    Hypertrophic scars (HTS) are a pathologic reaction of the skin and soft tissue to burn or other traumatic injury. Scar tissue can cause patients serious functional and cosmetic issues. Scar management strategies, specifically scar assessment techniques, are vital to improve clinical outcome. To date, no entirely objective method for scar assessment has been embraced by the medical community. In this study, we introduce for the first time, a novel polarized multispectral imaging system combining out-of-plane Stokes polarimetry and Spatial Frequency Domain Imaging (SFDI). This imaging system enables us to assess the pathophysiology (hemoglobin, blood oxygenation, water, and melanin) and structural features (cellularity and roughness) of HTS. To apply the proposed technique in an in vivo experiment, dermal wounds were created in a porcine model and allowed to form into scars. The developed scars were then measured at various time points using the imaging system. Results showed a good agreement with clinical Vancouver Scar Scale assessment and histological examinations. PMID:25360354

  13. Pollution detection by digital correlation of multispectral, stero-image pairs.

    NASA Technical Reports Server (NTRS)

    Krause, F. R.; Betz, H. T.; Lysobey, D. H.

    1971-01-01

    Remote detection of air pollution circulation patterns is proposed to eventually predict the accumulation of hazardous surface concentrations in time for preventive emission control operations. Earth observations from space platforms will contain information on the height, mean velocity and lateral mixing scales of inversion layers and pollution plumes. Although this information is often not visible on photographs, it could conceivably be retrieved through a digital cross-correlation of multispectral stereo image pairs. Laboratory and field test results are used to illustrate the detection of non-visual inversion layers, the reduction of dominant signal interference, and the spectroscopic identification of combustion products.

  14. High speed lookup table approach to radiometric calibration of multispectral image data

    NASA Technical Reports Server (NTRS)

    Kelly, W. L., IV; Meredith, B. D.; Howle, W. M.

    1980-01-01

    A concept for performing radiometric correction of multispectral image data onboard a spacecraft at very high data rates is presented and demonstrated. This concept utilized a lookup table approach, implemented in hardware, to convert the raw sensor data into the desired corrected output data. The digital lookup table memory was interfaced to a microprocessor to allow the data correction function to be completely programmable. Sensor data was processed with this approach at rates equal to the access time of the lookup table memory. This concept offers flexible high speed data processing for a wide range of applications and will benefit from the continuing improvements in performance of digital memories.

  15. A design approach to real-time formatting of high speed multispectral image data

    NASA Technical Reports Server (NTRS)

    Meredith, B. D.; Kelly, W. L., IV

    1981-01-01

    A design approach to formatting multispectral image data in real time at very high data rates is presented for future onboard processing applications. The approach employs a microprocessor-based alternating buffer memory configuration whose formatting function is completely programmable. Data are read from an output buffer in the desired format by applying the proper sequence of addresses to the buffer via a lookup table memory. Sensor data can be processed using this approach at rates limited by the buffer memory access time and the buffer switching process delay time. This design offers flexible high speed data processing and benefits from continuing increases in the performance of digital memories.

  16. CMOS Time-Resolved, Contact, and Multispectral Fluorescence Imaging for DNA Molecular Diagnostics

    PubMed Central

    Guo, Nan; Cheung, Ka Wai; Wong, Hiu Tung; Ho, Derek

    2014-01-01

    Instrumental limitations such as bulkiness and high cost prevent the fluorescence technique from becoming ubiquitous for point-of-care deoxyribonucleic acid (DNA) detection and other in-field molecular diagnostics applications. The complimentary metal-oxide-semiconductor (CMOS) technology, as benefited from process scaling, provides several advanced capabilities such as high integration density, high-resolution signal processing, and low power consumption, enabling sensitive, integrated, and low-cost fluorescence analytical platforms. In this paper, CMOS time-resolved, contact, and multispectral imaging are reviewed. Recently reported CMOS fluorescence analysis microsystem prototypes are surveyed to highlight the present state of the art. PMID:25365460

  17. Airborne Multispectral LIDAR Data for Land-Cover Classification and Land/water Mapping Using Different Spectral Indexes

    NASA Astrophysics Data System (ADS)

    Morsy, S.; Shaker, A.; El-Rabbany, A.; LaRocque, P. E.

    2016-06-01

    Airborne Light Detection And Ranging (LiDAR) data is widely used in remote sensing applications, such as topographic and landwater mapping. Recently, airborne multispectral LiDAR sensors, which acquire data at different wavelengths, are available, thus allows recording a diversity of intensity values from different land features. In this study, three normalized difference feature indexes (NDFI), for vegetation, water, and built-up area mapping, were evaluated. The NDFIs namely, NDFIG-NIR, NDFIG-MIR, and NDFINIR-MIR were calculated using data collected at three wavelengths; green: 532 nm, near-infrared (NIR): 1064 nm, and mid-infrared (MIR): 1550 nm by the world's first airborne multispectral LiDAR sensor "Optech Titan". The Jenks natural breaks optimization method was used to determine the threshold values for each NDFI, in order to cluster the 3D point data into two classes (water and land or vegetation and built-up area). Two sites at Scarborough, Ontario, Canada were tested to evaluate the performance of the NDFIs for land-water, vegetation, and built-up area mapping. The use of the three NDFIs succeeded to discriminate vegetation from built-up areas with an overall accuracy of 92.51%. Based on the classification results, it is suggested to use NDFIG-MIR and NDFINIR-MIR for vegetation and built-up areas extraction, respectively. The clustering results show that the direct use of NDFIs for land-water mapping has low performance. Therefore, the clustered classes, based on the NDFIs, are constrained by the recorded number of returns from different wavelengths, thus the overall accuracy is improved to 96.98%.

  18. The LANDSAT-1 multispectral scanner as a tool in the classification of inland lakes

    NASA Technical Reports Server (NTRS)

    Boland, D. H. P.; Blackwell, R. J.

    1975-01-01

    Relationships between LANDSAT-1 multispectral scanner (MSS) data and the trophic status of a group of lakes in the north-northeastern part of the United States were studied by predicting the magnitudes of two trophic state indicators, estimating lake position on a multivariate trophic scale, and automatically classifying lakes according to their trophic state. Initially, the principal component ordination was employed with 100 lakes. MSS data for some 20 lakes was then extracted from computer-compatible tapes (CCT) using a binary marking technique. The output was in the form of descriptive statistics and photographic concatenations. Color ratios were incorporated into regression models for the prediction of Secchi disc transparency, chlorophyll a, and lake position on the tropic scale. Results indicate that the LANDSAT-1 system, although handicapped by low spectral and spatial resolutions as well as excessive cloud cover, can be used as a supplemental data source in lake survey programs.

  19. Modular multispectral imaging system for multiple missions and applications

    NASA Astrophysics Data System (ADS)

    Schoonmaker, Jon; Podobna, Yuliya; Boucher, Cynthia; Saggese, Steve; Oakley, Daniel; Medeiros, Dustin

    2011-05-01

    The Navy recently began investing in the design of mission-specific payloads for the Small Tactical Unmanned Aircraft System (STUAS). STUAS is a Tier II size UAS with a roughly 35 pound mission payload and a gimbaled general-purpose electro optical/infrared (EO/IR) system. The EO/IR system is likely composed of a video camera in the visible, a mid-wave infrared (MWIR) and/or a long-wave infrared (LWIR) for night operations, and an infrared marker and laser range finder. Advanced Coherent Technologies, LLC (ACT), in a series of SBIR efforts, has developed a modular, multi-channel imaging system for deployment on airborne and UAV platforms. ACT's system, called EYE5, demonstrates how an EO/IR system combined with an on-board, real-time processor can be tailored for specific applications to produce real-time actionable data. The EYE5 sensor head and modular real-time processor descriptions are presented in this work. Examples of the system's abilities in various Navy-relevant applications are reviewed.

  20. An operative quantitative analysis of multispectral images of the eyeground

    NASA Astrophysics Data System (ADS)

    Lisenko, S. A.; Kugeiko, M. M.; Firago, V. A.; Kubarko, A. I.

    2014-09-01

    In the approximation of a four-layer model of the eyeground, we have studied the information content of photographs of the eyeground obtained in different spectral intervals from the visible range of the spectrum. We have shown that, under conditions of a priori uncertainty of all parameters of the eyeground that affect spectral fluxes of light multiply scattered by the eyeground, the two-dimensional distributions of the following parameters can be determined: (i) the contents of hemoglobin and macular pigment in the retina; (ii) the contents of melanin in the pigment epithelium and choroid; (iii) the degree of blood oxygenation; and (iv) the structural parameter of the retina, which characterizes the volume concentration of its effective scatterers. Based on results of a numerical simulation of the light-transfer process in the medium under study, we have determined regression relationships between parameters of the eyeground and spectral characteristics of its image and have proposed a method for the operative retrieval of parameter maps of the eyeground, which uses the determined regressions.

  1. 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.

  2. Integration of visible-through microwave-range multispectral image data sets for geologic mapping

    NASA Technical Reports Server (NTRS)

    Kruse, Fred A.; Dietz, John B.

    1991-01-01

    Multispectral remote sensing data sets collected during the Geologic Remote Sensing Field Experiment (GRSFE) conducted during 1989 in the southwestern U.S. were used to produce thematic image maps showing details of the surface geology. LANDSAT TM (Thematic Mapper) images were used to map the distribution of clays, carbonates, and iron oxides. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data were used to identify and map calcite, dolomite, sericite, hematite, and geothite, including mixtures. TIMS (Thermal Infrared Multispectral Scanner) data were used to map the distribution of igneous rock phases and carbonates based on their silica contents. AIRSAR (Airborne Synthetic Aperture Radar) data were used to map surface textures related to the scale of surface roughness. The AIRSAR also allowed identification of previously unmapped fault segments and structural control of lithology and minerology. Because all of the above data sets were geographically referenced, combination of different data types and direct comparison of the results with conventional field and laboratory data sets allowed improved geologic mapping of the test site.

  3. Thresholding for biological material detection in real-time multispectral imaging

    NASA Astrophysics Data System (ADS)

    Yoon, Seung Chul; Park, Bosoon; Lawrence, Kurt C.; Windham, William R.

    2005-09-01

    Recently, hyperspectral image analysis has proved successful for a target detection problem encountered in remote sensing as well as near sensing utilizing in situ instrumentation. The conventional global bi-level thresholding for target detection, such as the clustering-based Otsu's method, has been inadequate for the detection of biologically harmful material on foods that has a large degree of variability in size, location, color, shape, texture, and occurrence time. This paper presents multistep-like thresholding based on kernel density estimation for the real-time detection of harmful contaminants on a food product presented in multispectral images. We are particularly concerned with the detection of fecal contaminants on poultry carcasses in real-time. In the past, we identified 2 optimal wavelength bands and developed a real-time multispectral imaging system using a common aperture camera and a globally optimized thresholding method from a ratio of the optimal bands. This work extends our previous study by introducing a new decision rule to detect fecal contaminants on a single bird level. The underlying idea is to search for statistical separability along the two directions defined by the global optimal threshold vector and its orthogonal vector. Experimental results with real birds and fecal samples in different amounts are provided.

  4. Simulation of multispectral x-ray imaging scenarios by Wien shift optical spectroscopy

    NASA Astrophysics Data System (ADS)

    Brydegaard, M.; Svanberg, S.

    2010-02-01

    The acquisition of multispectral x-ray images and the treatment of such data are essential for understanding many devices that we encounter in everyday life. Examples include computerized tomography in hospitals and scanners at airports. X-ray devices remain impractical for undergraduate laboratories because of their considerable cost and the risk of exposure to ionizing radiation. One way to acquire spectral information and thus constituent-discriminating data in x-ray imaging is to alter the spectral contents of the illuminating x-ray source, which can be achieved by changing the x-ray tube voltage and thus energetically displacing the bremsstrahlung. A similar effect occurs in the emission from a black-body radiator in the optical and infrared regions when altering the temperature. We illustrate how to simulate the x-ray scenario with a webcam and an ordinary light bulb. Insight into how chemical and physical information regarding objects can be obtained in multispectral imaging supported by multivariate analysis is gained.

  5. Mariner 10 multispectral images of the eastern limb and farside of the moon

    NASA Technical Reports Server (NTRS)

    Robinson, Mark S.; Hawke, B. R.; Lucey, Paul G.; Smith, Gregory A.

    1992-01-01

    We present the first analysis of Mariner 10 multispectral image data for the eastern limb and farside of the moon. A new, rigorous calibration of these data is used that provides results that are consistent with independently derived data sets. From these newly calibrated data, a color ratio image (0.48/0.58 micron) is produced to show relative TiO2 abundances in mare units on the eastern limb and farside of the moon. Our results indicate that the farside lunar mare deposits examined have TiO2 contents that are intermediate to low (2.5 pct TiO2) relative to mare deposits on the nearside. We find no evidence for areally extensive deposits of either very low titanium or high to very high titanium basalts in these portions of the moon. Our findings support previous interpretations that the source regions for lunar mare basalts are laterally heterogeneous. Additionally, examination of highland units reveals a relatively blue region that corresponds with a previously proposed cryptomare, suggesting enhanced TiO2 for this unit. Finally, based on these Mariner 10 data, telescopic spectral data, Galileo multispectral image data, and lunar meteorite analyses we speculate that volcanism in farside mare deposits did not include high titanium basalts.

  6. Multispectral image restoration of historical documents based on LAAMs and mathematical morphology

    NASA Astrophysics Data System (ADS)

    Lechuga-S., Edwin; Valdiviezo-N., Juan C.; Urcid, Gonzalo

    2014-09-01

    This research introduces an automatic technique designed for the digital restoration of the damaged parts in historical documents. For this purpose an imaging spectrometer is used to acquire a set of images in the wavelength interval from 400 to 1000 nm. Assuming the presence of linearly mixed spectral pixels registered from the multispectral image, our technique uses two lattice autoassociative memories to extract the set of pure pigments conforming a given document. Through an spectral unmixing analysis, our method produces fractional abundance maps indicating the distributions of each pigment in the scene. These maps are then used to locate cracks and holes in the document under study. The restoration process is performed by the application of a region filling algorithm, based on morphological dilation, followed by a color interpolation to restore the original appearance of the filled areas. This procedure has been successfully applied to the analysis and restoration of three multispectral data sets: two corresponding to artificially superimposed scripts and a real data acquired from a Mexican pre-Hispanic codex, whose restoration results are presented.

  7. Multispectral Photography

    NASA Technical Reports Server (NTRS)

    1982-01-01

    Model II Multispectral Camera is an advanced aerial camera that provides optimum enhancement of a scene by recording spectral signatures of ground objects only in narrow, preselected bands of the electromagnetic spectrum. Its photos have applications in such areas as agriculture, forestry, water pollution investigations, soil analysis, geologic exploration, water depth studies and camouflage detection. The target scene is simultaneously photographed in four separate spectral bands. Using a multispectral viewer, such as their Model 75 Spectral Data creates a color image from the black and white positives taken by the camera. With this optical image analysis unit, all four bands are superimposed in accurate registration and illuminated with combinations of blue green, red, and white light. Best color combination for displaying the target object is selected and printed. Spectral Data Corporation produces several types of remote sensing equipment and also provides aerial survey, image processing and analysis and number of other remote sensing services.

  8. Multispectral Imaging of the Jet in 3C346

    NASA Astrophysics Data System (ADS)

    Smith, E. P.; Gardner, J. P.; Heap, S. R.; Baum, S. A.; O'Dea, C. P.

    1998-12-01

    Much progress has been made towards understanding the physics of extra-galactic jet flow in the past 10 years, through numerical modeling and detailed multi-frequency radio mapping. However, we still admit that basic parameters such as the magnetic field strength, the particle density, the bulk Lorentz factor and jet configuration (e.g., fast inner jet and slower outer sheath), and the nature and need for shocks remain unknown. However, by resolving the jets spectrally and spatially and pushing the observations to the higher energies with sufficient spatial resolution to resolve the cooling scale lengths, we can place unique constraints on the physical composition of, and acceleration mechanisms in, extra-galactic jets. The tightest constraints on the physics of jets come not from radio observations, but from observations in the optical, UV, and x-ray, where the lifetimes of the synchrotron emitting particles are measured in hundreds of years and the particles travel distances of hundreds of parsecs. To this end, we present radio through vacuum ultraviolet (STIS FUV-MAMA) spatially resolved images of the radio jet in 3C346 and use them examine the spatial variation of the spectral indices. Because the lifetime of the synchrotron radiating particles is inversely proportional to the observed frequency we can use the vastly different timescales (and therefore length scales) over which the radio (t ~ 10(5-7) yr.) and ultraviolet (t ~ 10(2-3) yr.) emitting particles cool to begin to place constraints on the magnetic field strengths. Also, the curvature of the spectrum as a function of position can be compared with aging models to constrain the magnetic field strength and to test the hypothesis that the magnetic field in radio jets may be substantially below equipartition (Heinz and Begelman 1997).

  9. Spot-5 multispectral image for 60-75 days of rice mapping

    NASA Astrophysics Data System (ADS)

    Amiruddin Ramli, Mohd; Shariff, Abdul Rashid Mohamed; Khairunniza Bejo, Siti

    2014-06-01

    The objective of this study is to investigate the potential application of Spot-5 multispectral satellite data in monitoring rice cultivation areas in IADA (Integrated Agriculture Development Area) located at Kerian District, Perak Malaysia. Information of the rice cultivation areas is a global economic and environmental significance. Multi-spectral images acquired at high spatial resolution are an important tool, especially in agricultural applications. This paper addresses the relationship between normalize difference vegetation index (NDVI) and ancillary data acquired from Farmers Organization Authority (PPK) for 217 farmer's field in IADA Kerian. The results indicated that NDVI range 0.62 - 0.75 has a strong positive relationship with the ground survey area estimation with (r = 0.85; p <0.01) (r2 = 0.722). The r2 value of 0.722 indicated a statistically significant linear relationship between the rice area estimate using NDVI range 0.62 - 0.75 and on the ground surveyed data for 217 farmers' fields. The equation of unstandardized distribution can be described as Ŷ=0.0197+0.852x. The equation for standardized regression formula for this distribution is Ŷ= 0.850x. Thus, the results indicate that 60-75 days of rice area can be estimated from the following equation Ŷ=0.197+0.852x, where Ŷ is the predicted rice area and x is area calculated using NDVI range 0.62-0.75 in IADA Kerian Perak Malaysia. The results appear promising and rice mapping operations using SPOT-5 multispectral image data can be foreseen.

  10. CubeSat modules for multispectral environmental imaging from polar orbit

    NASA Astrophysics Data System (ADS)

    Puschell, Jeffery J.; Stanton, Eric

    2012-10-01

    This paper describes an approach for addressing potential gaps in continuity of critical weather and other Earth observations begun by SeaWiFS, MODIS and MISR with single band CubeSat imagers that could be used in combination either onboard a single host satellite or in a constellation of small satellites to provide stable, high SNR multispectral measurements. These wide field of view, high SNR imagers are enabled by large format (~4000 element long) ultraviolet-near infrared focal plane assemblies and achromatic wide field of view telescopes. This new class of wide field of view pushbroom imagers offers capability to continue SeaWiFS, MODIS, MISR and, as discussed in a companion paper, DMSP OLS measurements in support of operational weather observations, Earth imaging and Earth science studies. In addition, extension of the spectral response to 350 nm enables supplementing existing and future systems with multispectral observations at near ultraviolet wavelengths of importance to aerosol and ocean color measurements. The large format size of the array and high optical quality wide field of view telescope enable an entire ~3000 km wide MODIS or VIIRS swath to be collected simultaneously by a pushbroom imaging radiometer in polar sun synchronous orbit. The increase in effective integration time made possible by a pushbroom approach versus the whiskbroom imaging approaches of AVHRR, MODIS and VIIRS enables measurements with the required SNR using a telescope aperture so small that the entire instrument can fit on a 3U CubeSat. Small single purpose imager modules like this could be used to supplement much larger systems like VIIRS by providing measurements of scientifically important bands not in VIIRS like the MODIS chlorophyll fluorescence and near-infrared water vapor bands and fill possible gaps in measurement continuity not provided by future systems or resulting from program delays.

  11. Segmentation and visualization of brain lesions in multispectral magnetic resonance images.

    PubMed

    Holden, M; Steen, E; Lundervold, A

    1995-01-01

    In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool. PMID:7780944

  12. Multispectral imaging in the extended near-infrared window based on endogenous chromophores

    PubMed Central

    Cao, Qian; Zhegalova, Natalia G.; Wang, Steven T.; Akers, Walter J.

    2013-01-01

    Abstract. To minimize the problem with scattering in deep tissues while increasing the penetration depth, we explored the feasibility of imaging in the relatively unexplored extended near infrared (exNIR) spectral region at 900 to 1400 nm with endogenous chromophores. This region, also known as the second NIR window, is weakly dominated by absorption from water and lipids and is free from other endogenous chromophores with virtually no autofluorescence. To demonstrate the applicability of the exNIR for bioimaging, we analyzed the optical properties of individual components and biological tissues using an InGaAs spectrophotometer and a multispectral InGaAs scanning imager featuring transmission geometry. Based on the differences in spectral properties of tissues, we utilized ratiometric approaches to extract spectral characteristics from the acquired three-dimensional “datacube”. The obtained images of an exNIR transmission through a mouse head revealed sufficient details consistent with anatomical structures. PMID:23933967

  13. A methodology for evaluation of an interactive multispectral image processing system

    NASA Technical Reports Server (NTRS)

    Kovalick, William M.; Newcomer, Jeffrey A.; Wharton, Stephen W.

    1987-01-01

    Because of the considerable cost of an interactive multispectral image processing system, an evaluation of a prospective system should be performed to ascertain if it will be acceptable to the anticipated users. Evaluation of a developmental system indicated that the important system elements include documentation, user friendliness, image processing capabilities, and system services. The criteria and evaluation procedures for these elements are described herein. The following factors contributed to the success of the evaluation of the developmental system: (1) careful review of documentation prior to program development, (2) construction and testing of macromodules representing typical processing scenarios, (3) availability of other image processing systems for referral and verification, and (4) use of testing personnel with an applications perspective and experience with other systems. This evaluation was done in addition to and independently of program testing by the software developers of the system.

  14. Novel Algorithm for Classification of Medical Images

    NASA Astrophysics Data System (ADS)

    Bhushan, Bharat; Juneja, Monika

    2010-11-01

    Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

  15. Multispectral imaging of the ocular fundus using light emitting diode illumination

    NASA Astrophysics Data System (ADS)

    Everdell, N. L.; Styles, I. B.; Calcagni, A.; Gibson, J.; Hebden, J.; Claridge, E.

    2010-09-01

    We present an imaging system based on light emitting diode (LED) illumination that produces multispectral optical images of the human ocular fundus. It uses a conventional fundus camera equipped with a high power LED light source and a highly sensitive electron-multiplying charge coupled device camera. It is able to take pictures at a series of wavelengths in rapid succession at short exposure times, thereby eliminating the image shift introduced by natural eye movements (saccades). In contrast with snapshot systems the images retain full spatial resolution. The system is not suitable for applications where the full spectral resolution is required as it uses discrete wavebands for illumination. This is not a problem in retinal imaging where the use of selected wavelengths is common. The modular nature of the light source allows new wavelengths to be introduced easily and at low cost. The use of wavelength-specific LEDs as a source is preferable to white light illumination and subsequent filtering of the remitted light as it minimizes the total light exposure of the subject. The system is controlled via a graphical user interface that enables flexible control of intensity, duration, and sequencing of sources in synchrony with the camera. Our initial experiments indicate that the system can acquire multispectral image sequences of the human retina at exposure times of 0.05 s in the range of 500-620 nm with mean signal to noise ratio of 17 dB (min 11, std 4.5), making it suitable for quantitative analysis with application to the diagnosis and screening of eye diseases such as diabetic retinopathy and age-related macular degeneration.

  16. Galileo multispectral imaging of the north polar and eastern limb regions of the moon

    USGS Publications Warehouse

    Belton, M.J.S.; Greeley, R.; Greenberg, R.; McEwen, A.; Klaasen, K.P.; Head, J. W.; Pieters, C.; Neukum, G.; Chapman, C.R.; Geissler, P.; Heffernan, C.; Breneman, H.; Anger, C.; Carr, M.H.; Davies, M.E.; Fanale, F.P.; Gierasch, P.J.; Ingersoll, A.P.; Johnson, T.V.; Pilcher, C.B.; Thompson, W.R.; Veverka, J.; Sagan, C.

    1994-01-01

    Multispectral images obtained during the Galileo probe's second encounter with the moon reveal the compositional nature of the north polar regions and the northeastern limb. Mare deposits in these regions are found to be primarily low to medium titanium lavas and, as on the western limb, show only slight spectral heterogeneity. The northern light plains are found to have the spectral characteristics of highlands materials, show little evidence for the presence of cryptomaria, and were most likely emplaced by impact processes regardless of their age.Multispectral images obtained during the Galileo probe's second encounter with the moon reveal the compositional nature of the north polar regions and the northeastern limb. Mare deposits in these regions are found to be primarily low to medium titanium lavas and, as on the western limb, show only slight spectral heterogeneity. The northern light plains are found to have the spectral characteristics of highlands materials, show little evidence for the presence of cryptomaria, and were most likely emplaced by impact processes regardless of their age.

  17. Skin Parameter Map Retrieval from a Dedicated Multispectral Imaging System Applied to Dermatology/Cosmetology

    PubMed Central

    2013-01-01

    In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced. PMID:24159326

  18. Multispectral fluorescence imaging of human ovarian and fallopian tube tissue for early-stage cancer detection

    NASA Astrophysics Data System (ADS)

    Tate, Tyler H.; Baggett, Brenda; Rice, Photini F. S.; Koevary, Jennifer Watson; Orsinger, Gabriel V.; Nymeyer, Ariel C.; Welge, Weston A.; Saboda, Kathylynn; Roe, Denise J.; Hatch, Kenneth D.; Chambers, Setsuko K.; Utzinger, Urs; Barton, Jennifer Kehlet

    2016-05-01

    With early detection, 5-year survival rates for ovarian cancer exceed 90%, yet no effective early screening method exists. Emerging consensus suggests over 50% of the most lethal form of the disease originates in the fallopian tube. Twenty-eight women undergoing oophorectomy or debulking surgery provided informed consent for the use of surgical discard tissue samples for multispectral fluorescence imaging. Using multiple ultraviolet and visible excitation wavelengths and emissions bands, 12 fluorescence and 6 reflectance images of 47 ovarian and 31 fallopian tube tissue samples were recorded. After imaging, each sample was fixed, sectioned, and stained for pathological evaluation. Univariate logistic regression showed cancerous tissue samples had significantly lower intensity than noncancerous tissue for 17 image types. The predictive power of multiple image types was evaluated using multivariate logistic regression (MLR) and quadratic discriminant analysis (QDA). Two MLR models each using two image types had receiver operating characteristic curves with area under the curve exceeding 0.9. QDA determined 56 image type combinations with perfect resubstituting using as few as five image types. Adaption of the system for future in vivo fallopian tube and ovary endoscopic imaging is possible, which may enable sensitive detection of ovarian cancer with no exogenous contrast agents.

  19. Crop classification using multi-temporal HJ satellite images: case study in Kashgar, Xinjiang

    NASA Astrophysics Data System (ADS)

    Hao, Pengyu; Niu, Zheng; Wang, Li

    2014-11-01

    The HJ satellite constellation, characterized as high temporal resolution (4 day revisit frequency), has high potential to obtain cloud-free images covering all cruel periods for crop classification during growing season. In this paper, three HJ images (in May, July and September) were acquired, the performances of different multi-spectral HJ CCD data combinations for crop classification in Kashgar, Xinjiang were estimated using library for Support Vector Machine (LIBSVM), and ground reference data obtained in 2011 field work were used as training and validation samples. The result showed that multi-temporal HJ data has a potential to classify crops with an overall classification accuracy of 93.77%. Among the three time periods utilized in this research, the image acquired in July achieved the highest overall accuracy (86.98%) because all summer crops were under dense canopy closure. Cotton could be accurately extracted in May image (both user and produce accuracy are above 90%) because of its lower canopy closure compared with spring, the rotate crop (wheat_maize) and winter crop (wheat) at the time period. Then, the July and September combination performed as good as that of all threetime- period combination, which indicated that images obtained at cruel time periods are enough to identify crops, and the additional images improve little on classification accuracy. In addition, multi-temporal NDVI in cruel time periods of the growing season is testified efficient to classify crops with significant phenonlogical variances since they achieved similar overall accuracy to that of multi-temporal multi-spectral combination.

  20. 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.

  1. Deep Reconstruction Models for Image Set Classification.

    PubMed

    Hayat, Munawar; Bennamoun, Mohammed; An, Senjian

    2015-04-01

    Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods. PMID:26353289

  2. Rapid Assessment of Tablet Film Coating Quality by Multispectral UV Imaging.

    PubMed

    Klukkert, Marten; Wu, Jian X; Rantanen, Jukka; Rehder, Soenke; Carstensen, Jens M; Rades, Thomas; Leopold, Claudia S

    2016-08-01

    Chemical imaging techniques are beneficial for control of tablet coating layer quality as they provide spectral and spatial information and allow characterization of various types of coating defects. The purpose of this study was to assess the applicability of multispectral UV imaging for assessment of the coating layer quality of tablets. UV images were used to detect, characterize, and localize coating layer defects such as chipped parts, inhomogeneities, and cracks, as well as to evaluate the coating surface texture. Acetylsalicylic acid tablets were prepared on a rotary tablet press and coated with a polyvinyl alcohol-polyethylene glycol graft copolymer using a pan coater. It was demonstrated that the coating intactness can be assessed accurately and fast by UV imaging. The different types of coating defects could be differentiated and localized based on multivariate image analysis and Soft Independent Modeling by Class Analogy applied to the UV images. Tablets with inhomogeneous texture of the coating could be identified and distinguished from those with a homogeneous surface texture. Consequently, UV imaging was shown to be well-suited for monitoring of the tablet coating layer quality. UV imaging is a promising technique for fast quality control of the tablet coating because of the high data acquisition speed and its nondestructive analytical nature.

  3. Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter

    NASA Astrophysics Data System (ADS)

    Bonny, Jean-Marie; Boespflug-Tanguly, Odile; Zanca, Michel; Renou, Jean-Pierre

    2003-03-01

    A solution for discrete multi-exponential analysis of T2 relaxation decay curves obtained in current multi-echo imaging protocol conditions is described. We propose a preprocessing step to improve the signal-to-noise ratio and thus lower the signal-to-noise ratio threshold from which a high percentage of true multi-exponential detection is detected. It consists of a multispectral nonlinear edge-preserving filter that takes into account the signal-dependent Rician distribution of noise affecting magnitude MR images. Discrete multi-exponential decomposition, which requires no a priori knowledge, is performed by a non-linear least-squares procedure initialized with estimates obtained from a total least-squares linear prediction algorithm. This approach was validated and optimized experimentally on simulated data sets of normal human brains.

  4. Charon's Color: A view from New Horizon Ralph/Multispectral Visible Imaging Camera

    NASA Astrophysics Data System (ADS)

    Olkin, C.; Howett, C.; Grundy, W. M.; Parker, A. H.; Ennico Smith, K.; Stern, S. A.; Binzel, R. P.; Cook, J. C.; Cruikshank, D. P.; Dalle Ore, C.; Earle, A. M.; Jennings, D. E.; Linscott, I.; Lunsford, A.; Parker, J. W.; Protopapa, S.; Reuter, D.; Singer, K. N.; Spencer, J. R.; Tsang, C.; Verbiscer, A.; Weaver, H. A., Jr.; Young, L. A.

    2015-12-01

    The Multispectral Visible Imaging Camera (MVIC; Reuter et al., 2008) is part of Ralph, an instrument on NASA's New Horizons spacecraft. MVIC is the color 'eyes' of New Horizons, observing objects using five bands from blue to infrared wavelengths. MVIC's images of Charon show it to be an intriguing place, a far cry from the grey heavily cratered world once postulated. Rather Charon is observed to have large surface areas free of craters, and a northern polar region that is much redder than its surroundings. This talk will describe these initial results in more detail, along with Charon's global geological color variations to put these results into their wider context. Finally possible surface coloration mechanisms due to global processes and/or seasonal cycles will be discussed.

  5. Archiving shape and appearance of cultural heritage objects using structured light projection and multispectral imaging

    NASA Astrophysics Data System (ADS)

    Sitnik, Robert; Krzesłowski, Jakub; Maczkowski, Grzegorz

    2012-02-01

    To create faithful reproduction of a cultural heritage object, it is crucial to gather information on intrinsic optical properties of the object's surface, as well as its geometry. An integrated device has been developed that performs a three-dimensional measurement using structured light projection, followed by multispectral imaging for precise color retrieval and directional illumination for estimating bidirectional reflectance distribution function (BRDF) parameters. The main advantage shown in this work is the use of only one detector during the whole acquisition process to assure ideal correspondence of multimodal surface data in the image space. A method is shown for performing the measurement using an integrated device. Methods of data organization and processing are described facilitating robust operation of the developed software. A prototype setup for the integrated system is presented together with measurement parameters and sample measurement.

  6. Low-cost multispectral vegetation imaging system for detecting leaking CO₂ gas.

    PubMed

    Hogan, Justin A; Shaw, Joseph A; Lawrence, Rick L; Larimer, Randal M

    2012-02-01

    As a component of a multisensor approach to monitoring carbon sequestration sites for possible leaks of the CO₂ gas from underground reservoirs, a low-cost multispectral imaging system has been developed for indirect detection of gas leaks through observations of the resulting stress in overlying vegetation. The imager employs front-end optics designed to provide a full 50° field of view with a small, low-cost CMOS detector, while still maintaining quasi-collimated light through the angle-dependent interference filters used to define the spectral bands. Red and near-infrared vegetation reflectances are used to compute the normalized difference vegetation index (NDVI) and spatial and temporal patterns are analyzed statistically to identify regions of anomalous stress, which are then flagged for closer inspection with in-situ CO₂ sensors. The system is entirely self-contained with an onboard compact computer and is housed in a weather-proof housing to enable extended outdoor deployment.

  7. Fast Image Texture Classification Using Decision Trees

    NASA Technical Reports Server (NTRS)

    Thompson, David R.

    2011-01-01

    Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

  8. 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.

  9. Multispectral imaging of tissue absorption and scattering using spatial frequency domain imaging and a computed-tomography imaging spectrometer

    NASA Astrophysics Data System (ADS)

    Weber, Jessie R.; Cuccia, David J.; Johnson, William R.; Bearman, Gregory H.; Durkin, Anthony J.; Hsu, Mike; Lin, Alexander; Binder, Devin K.; Wilson, Dan; Tromberg, Bruce J.

    2011-01-01

    We present an approach for rapidly and quantitatively mapping tissue absorption and scattering spectra in a wide-field, noncontact imaging geometry by combining multifrequency spatial frequency domain imaging (SFDI) with a computed-tomography imaging spectrometer (CTIS). SFDI overcomes the need to spatially scan a source, and is based on the projection and analysis of periodic structured illumination patterns. CTIS provides a throughput advantage by simultaneously diffracting multiple spectral images onto a single CCD chip to gather spectra at every pixel of the image, thus providing spatial and spectral information in a single snapshot. The spatial-spectral data set was acquired 30 times faster than with our wavelength-scanning liquid crystal tunable filter camera, even though it is not yet optimized for speed. Here we demonstrate that the combined SFDI-CTIS is capable of rapid, multispectral imaging of tissue absorption and scattering in a noncontact, nonscanning platform. The combined system was validated for 36 wavelengths between 650-1000 nm in tissue simulating phantoms over a range of tissue-like absorption and scattering properties. The average percent error for the range of absorption coefficients (μa) was less than 10% from 650-800 nm, and less than 20% from 800-1000 nm. The average percent error in reduced scattering coefficients (μs') was less than 5% from 650-700 nm and less than 3% from 700-1000 nm. The SFDI-CTIS platform was applied to a mouse model of brain injury in order to demonstrate the utility of this approach in characterizing spatially and spectrally varying tissue optical properties.

  10. From multispectral imaging of autofluorescence to chemical and sensory images of lipid oxidation in cod caviar paste.

    PubMed

    Airado-Rodríguez, Diego; Høy, Martin; Skaret, Josefine; Wold, Jens Petter

    2014-05-01

    The potential of multispectral imaging of autofluorescence to map sensory flavour properties and fluorophore concentrations in cod caviar paste has been investigated. Cod caviar paste was used as a case product and it was stored over time, under different headspace gas composition and light exposure conditions, to obtain a relevant span in lipid oxidation and sensory properties. Samples were divided in two sets, calibration and test sets, with 16 and 7 samples, respectively. A third set of samples was prepared with induced gradients in lipid oxidation and sensory properties by light exposure of certain parts of the sample surface. Front-face fluorescence emission images were obtained for excitation wavelength 382 nm at 11 different channels ranging from 400 to 700 nm. The analysis of the obtained sets of images was divided in two parts: First, in an effort to compress and extract relevant information, multivariate curve resolution was applied on the calibration set and three spectral components and their relative concentrations in each sample were obtained. The obtained profiles were employed to estimate the concentrations of each component in the images of the heterogeneous samples, giving chemical images of the distribution of fluorescent oxidation products, protoporphyrin IX and photoprotoporphyrin. Second, regression models for sensory attributes related to lipid oxidation were constructed based on the spectra of homogeneous samples from the calibration set. These models were successfully validated with the test set. The models were then applied for pixel-wise estimation of sensory flavours in the heterogeneous images, giving rise to sensory images. As far as we know this is the first time that sensory images of odour and flavour are obtained based on multispectral imaging. PMID:24720964

  11. Blind source separation of ex-vivo aorta tissue multispectral images.

    PubMed

    Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

    2015-05-01

    Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method's performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue.

  12. Blind source separation of ex-vivo aorta tissue multispectral images

    PubMed Central

    Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

    2015-01-01

    Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method’s performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

  13. Blind source separation of ex-vivo aorta tissue multispectral images.

    PubMed

    Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

    2015-05-01

    Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method's performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

  14. Localization and relative quantification of carbon nanotubes in cells with multispectral imaging flow cytometry.

    PubMed

    Marangon, Iris; Boggetto, Nicole; Ménard-Moyon, Cécilia; Luciani, Nathalie; Wilhelm, Claire; Bianco, Alberto; Gazeau, Florence

    2013-01-01

    Carbon-based nanomaterials, like carbon nanotubes (CNTs), belong to this type of nanoparticles which are very difficult to discriminate from carbon-rich cell structures and de facto there is still no quantitative method to assess their distribution at cell and tissue levels. What we propose here is an innovative method allowing the detection and quantification of CNTs in cells using a multispectral imaging flow cytometer (ImageStream, Amnis). This newly developed device integrates both a high-throughput of cells and high resolution imaging, providing thus images for each cell directly in flow and therefore statistically relevant image analysis. Each cell image is acquired on bright-field (BF), dark-field (DF), and fluorescent channels, giving access respectively to the level and the distribution of light absorption, light scattered and fluorescence for each cell. The analysis consists then in a pixel-by-pixel comparison of each image, of the 7,000-10,000 cells acquired for each condition of the experiment. Localization and quantification of CNTs is made possible thanks to some particular intrinsic properties of CNTs: strong light absorbance and scattering; indeed CNTs appear as strongly absorbed dark spots on BF and bright spots on DF with a precise colocalization. This methodology could have a considerable impact on studies about interactions between nanomaterials and cells given that this protocol is applicable for a large range of nanomaterials, insofar as they are capable of absorbing (and/or scattering) strongly enough the light. PMID:24378540

  15. Multispectral fluorescence imaging of human ovarian and Fallopian tube tissue for early stage cancer detection

    NASA Astrophysics Data System (ADS)

    Tate, Tyler; Baggett, Brenda; Rice, Photini; Watson, Jennifer; Orsinger, Gabe; Nymeyer, Ariel C.; Welge, Weston A.; Keenan, Molly; Saboda, Kathylynn; Roe, Denise J.; Hatch, Kenneth; Chambers, Setsuko; Black, John; Utzinger, Urs; Barton, Jennifer

    2015-03-01

    With early detection, five year survival rates for ovarian cancer are over 90%, yet no effective early screening method exists. Emerging consensus suggests that perhaps over 50% of the most lethal form of the disease, high grade serous ovarian cancer, originates in the Fallopian tube. Cancer changes molecular concentrations of various endogenous fluorophores. Using specific excitation wavelengths and emissions bands on a Multispectral Fluorescence Imaging (MFI) system, spatial and spectral data over a wide field of view can be collected from endogenous fluorophores. Wavelength specific reflectance images provide additional information to normalize for tissue geometry and blood absorption. Ratiometric combination of the images may create high contrast between neighboring normal and abnormal tissue. Twenty-six women undergoing oophorectomy or debulking surgery consented the use of surgical discard tissue samples for MFI imaging. Forty-nine pieces of ovarian tissue and thirty-two pieces of Fallopian tube tissue were collected and imaged with excitation wavelengths between 280 nm and 550 nm. After imaging, each tissue sample was fixed, sectioned and HE stained for pathological evaluation. Comparison of mean intensity values between normal, benign, and cancerous tissue demonstrate a general trend of increased fluorescence of benign tissue and decreased fluorescence of cancerous tissue when compared to normal tissue. The predictive capabilities of the mean intensity measurements are tested using multinomial logistic regression and quadratic discriminant analysis. Adaption of the system for in vivo Fallopian tube and ovary endoscopic imaging is possible and is briefly described.

  16. Predicting Electron Energy Flux Using Ground-Based Multi-Spectral Auroral Imaging

    NASA Astrophysics Data System (ADS)

    Grubbs, G. A.; Samara, M.; Michell, R.; Redmon, R. J.

    2013-12-01

    High-resolution, multi-spectral auroral observations can now be routinely acquired using the Multi-spectral Observatory Of Sensitive EMCCDs (MOOSE), currently installed in Poker Flat, AK. Observations from the past 2 auroral seasons have yielded many simultaneous auroral observations in 4 different emission lines (427.8 nm, 557.7 nm, 630 nm, and 844.6 nm). From these data, the brightness of the absolute auroral emissions will be calculated. Combined with atmospheric modeling, auroral emission brightness will be used to predict the total energy flux and characteristic energy of the electrons responsible for the aurora. The theory behind this method is only developed for auroral measurements in the magnetic zenith, and therefore it is not known to what extent it can be applied off zenith. All-sky auroral image data will be examined and compared with DMSP satellite overpasses to quantify the extent to which the model can make predictions off-zenith, creating an empirical model that could then be applied to the many cases without overpasses. This will lead to large-scale 2-D maps of electron precipitation characteristics which can contribute to global ionospheric models.

  17. Multispectral upconversion luminescence intensity ratios for ascertaining the tissue imaging depth

    NASA Astrophysics Data System (ADS)

    Liu, Kai; Wang, Yu; Kong, Xianggui; Liu, Xiaomin; Zhang, Youlin; Tu, Langping; Ding, Yadan; Aalders, Maurice C. G.; Buma, Wybren Jan; Zhang, Hong

    2014-07-01

    Upconversion nanoparticles (UCNPs) have in recent years emerged as excellent contrast agents for in vivo luminescence imaging of deep tissues. But information abstracted from these images is in most cases restricted to 2-dimensions, without the depth information. In this work, a simple method has been developed to accurately ascertain the tissue imaging depth based on the relative luminescence intensity ratio of multispectral NaYF4:Yb3+,Er3+ UCNPs. A theoretical mode was set up, where the parameters in the quantitative relation between the relative intensities of the upconversion luminescence spectra and the depth of the UCNPs were determined using tissue mimicking liquid phantoms. The 540 nm and 650 nm luminescence intensity ratios (G/R ratio) of NaYF4:Yb3+,Er3+ UCNPs were monitored following excitation path (Ex mode) and emission path (Em mode) schemes, respectively. The model was validated by embedding NaYF4:Yb3+,Er3+ UCNPs in layered pork muscles, which demonstrated a very high accuracy of measurement in the thickness up to centimeter. This approach shall promote significantly the power of nanotechnology in medical optical imaging by expanding the imaging information from 2-dimensional to real 3-dimensional.Upconversion nanoparticles (UCNPs) have in recent years emerged as excellent contrast agents for in vivo luminescence imaging of deep tissues. But information abstracted from these images is in most cases restricted to 2-dimensions, without the depth information. In this work, a simple method has been developed to accurately ascertain the tissue imaging depth based on the relative luminescence intensity ratio of multispectral NaYF4:Yb3+,Er3+ UCNPs. A theoretical mode was set up, where the parameters in the quantitative relation between the relative intensities of the upconversion luminescence spectra and the depth of the UCNPs were determined using tissue mimicking liquid phantoms. The 540 nm and 650 nm luminescence intensity ratios (G/R ratio) of NaYF4:Yb3

  18. Texture classification of lung computed tomography images

    NASA Astrophysics Data System (ADS)

    Pheng, Hang See; Shamsuddin, Siti M.

    2013-03-01

    Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

  19. MULTISPECTRAL LINE-SCAN IMAGING SYSTEM FOR SIMULTANEOUS FLUORESCENCE AND REFLECTANCE MEASUREMENTS OF APPLES: MULTITASK APPLE INSPECTION SYSTEM

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this methodology paper, a recently developed line-scan imaging system, capable of simultaneously acquiring a combination of multispectral reflectance and fluorescence from fast moving objects, is presented. The system can potentially provide multitask inspections for quality and safety attribute...

  20. Radical advancement in multi-spectral imaging for autonomous vehicles (UAVs, UGVs, and UUVs) using active compensation.

    SciTech Connect

    Clark, Brian F.; Bagwell, Brett E.; Wick, David Victor

    2007-01-01

    The purpose of this LDRD was to demonstrate a compact, multi-spectral, refractive imaging systems using active optical compensation. Compared to a comparable, conventional lens system, our system has an increased operational bandwidth, provides for spectral selectivity and, non-mechanically corrects aberrations induced by the wavelength dependent properties of a passive refractive optical element (i.e. lens). The compact nature and low power requirements of the system lends itself to small platforms such as autonomous vehicles. In addition, the broad spectral bandwidth of our system would allow optimized performance for both day/night use, and the multi-spectral capability allows for spectral discrimination and signature identification.

  1. Evaluation of port-wine stain treatment outcomes using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Samatham, Ravikant; Choudhury, Niloy; Krol, Alfons L.; Jacques, Steven L.

    2012-02-01

    Port-wine Stain (PWS) is a vascular malformation characterized by ectasia of superficial dermal capillaries. The flash-lamp pumped pulsed dye laser (PDL) treatment has been the mainstay of PWS for the last decade. Despite the success of the PDL in significantly fading the PWS, the overall cure rate is less than 10%. The precise efficacy of an individual PDL treatment is hard to evaluate and the treatment outcome is measured by visual observation of clinical fading. A hand-held multi-spectral imaging system was developed to image PWS before and after PDL treatment. In an NIH-funded pilot study multi-spectral camera was used to image PWS in children (2- 17 years). Oxygen saturation (S) and blood content (B) of PWS before and after the treatment was determined by analysis of the reflectance spectra. The outcome of the treatment was evaluated during follow up visits of the patients. One of the major causes of failure of laser therapy of port-wine stains (PWS) is reperfusion of the lesion after laser treatment. Oxygen saturation and blood content maps of PWS before and after treatment can predict regions of reperfusion and subsequent failure of the treatment. The ability to measure reperfusion and to predict lesions or areas susceptible to reperfusion, will help in selection of patients/lesions for laser treatment and help to optimize laser dosimetry for maximum effect. The current studies also should provide a basis for monitoring of future alternative therapies or enhancers of laser treatment in resistant cases.

  2. Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

    NASA Astrophysics Data System (ADS)

    Harvey, Neal R.; Levenson, Richard M.; Rimm, David L.

    2003-05-01

    Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

  3. Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition

    PubMed Central

    Park, Chulhee; Kang, Moon Gi

    2016-01-01

    A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors. PMID:27213381

  4. Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Liebel, L.; Körner, M.

    2016-06-01

    In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for single-image super resolution are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of deep learning techniques, such as convolutional neural networks (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, end-to-end learning is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.

  5. Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition.

    PubMed

    Park, Chulhee; Kang, Moon Gi

    2016-01-01

    A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors. PMID:27213381

  6. A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation

    SciTech Connect

    Theiler, J.; Gisler, G.

    1997-07-01

    The recent and continuing construction of multi and hyper spectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earth bound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one kind of intermediate form which provides some measure of data compression. Traditional image segmentation algorithms treat pixels independently and cluster the pixels according only to their spectral information. This neglects the implicit spatial information that is available in the image. We will suggest a simple approach; a variant of the standard k-means algorithm which uses both spatial and spectral properties of the image. The segmented image has the property that pixels which are spatially contiguous are more likely to be in the same class than are random pairs of pixels. This property naturally comes at some cost in terms of the compactness of the clusters in the spectral domain, but we have found that the spatial contiguity and spectral compactness properties are nearly orthogonal, which means that we can make considerable improvements in the one with minimal loss in the other.

  7. Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition.

    PubMed

    Park, Chulhee; Kang, Moon Gi

    2016-05-18

    A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors.

  8. Ex vivo photometric and polarimetric multilayer characterization of human healthy colon by multispectral Mueller imaging.

    PubMed

    Pierangelo, Angelo; Manhas, Sandeep; Benali, Abdelali; Fallet, Clément; Antonelli, Maria-Rosaria; Novikova, Tatiana; Gayet, Brice; Validire, Pierre; De Martino, Antonello

    2012-06-01

    Healthy human colon samples were analyzed ex vivo with a multispectral imaging Mueller polarimeter operating from 500 to 700 nm in a backscattering configuration with diffuse light illumination impinging on the innermost tissue layer, the mucosa. The intensity and polarimetric responses were taken on whole tissues first and after progressive exfoliation of the outer layers afterwards. Moreover, these measurements were carried out with two different substrates (one bright and the other dark) successively placed beneath each sample, allowing a reasonably accurate evaluation of the contributions to the overall backscattered light by the various layers. For the shorter investigated wavelengths (500 to 550 nm) the major contribution comes from mucosa and submucosa, while for the longer wavelengths (650 to 700 nm) muscular tissue and fat also contribute significantly. The depolarization has also been studied and is found to be stronger in the red part of the spectrum, mainly due to the highly depolarizing power of the muscular and fat layers. PMID:22734765

  9. Miniature spectrometer and multispectral imager as a potential diagnostic aid in dermatology

    NASA Astrophysics Data System (ADS)

    Zeng, Haishan; MacAulay, Calum E.; McLean, David I.; Lui, Harvey; Palcic, Branko

    1995-04-01

    A miniature spectrometer system has been constructed for both reflectance and autofluorescence spectral measurements of skin. The system is based on PC plug-in spectrometer, therefore, it is miniature and easy to operate. The spectrometer has been used clinically to collect spectral data from various skin lesions including skin cancer. To date, 48 patients with a total of 71 diseased skin sites have been measured. Analysis of these preliminary data suggests that unique spectral characteristics exist for certain types of skin lesions, i.e. seborrheic keratosis, psoriasis, etc.. These spectral characteristics will help the differential diagnosis in Dermatology practice. In conjunction with the spectral point measurements, we are building and testing a multispectral imaging system to measure the spatial distribution of skin reflectance and autofluorescence. Preliminary results indicate that a cutaneous squamous cell carcinoma has a weak autofluorescence signal at the edge of the lesion, but a higher autofluorescence signal in the central area.

  10. Joint pattern recognition/data compression concept for ERTS multispectral imaging

    NASA Technical Reports Server (NTRS)

    Hilbert, E. E.

    1975-01-01

    This paper describes a new technique which jointly applies clustering and source encoding concepts to obtain data compression. The cluster compression technique basically uses clustering to extract features from the measurement data set which are used to describe characteristics of the entire data set. In addition, the features may be used to approximate each individual measurement vector by forming a sequence of scalar numbers which define each measurement vector in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. A description of a practical cluster compression algorithm is given and experimental results are presented to show trade-offs and characteristics of various implementations. Examples are provided which demonstrate the application of cluster compression to multispectral image data of the Earth Resources Technology Satellite.

  11. MONSTIR II: A 32-channel, multispectral, time-resolved optical tomography system for neonatal brain imaging

    SciTech Connect

    Cooper, Robert J. Magee, Elliott; Everdell, Nick; Magazov, Salavat; Varela, Marta; Airantzis, Dimitrios; Gibson, Adam P.; Hebden, Jeremy C.

    2014-05-15

    We detail the design, construction and performance of the second generation UCL time-resolved optical tomography system, known as MONSTIR II. Intended primarily for the study of the newborn brain, the system employs 32 source fibres that sequentially transmit picosecond pulses of light at any four wavelengths between 650 and 900 nm. The 32 detector channels each contain an independent photo-multiplier tube and temporally correlated photon-counting electronics that allow the photon transit time between each source and each detector position to be measured with high temporal resolution. The system's response time, temporal stability, cross-talk, and spectral characteristics are reported. The efficacy of MONSTIR II is demonstrated by performing multi-spectral imaging of a simple phantom.

  12. A scan-angle correction for thermal infrared multispectral data using side lapping images

    USGS Publications Warehouse

    Watson, K.

    1996-01-01

    Thermal infrared multispectral scanner (TIMS) images, acquired with side lapping flight lines, provide dual angle observations of the same area on the ground and can thus be used to estimate variations in the atmospheric transmission with scan angle. The method was tested using TIMS aircraft data for six flight lines with about 30% sidelap for an area within Joshua Tree National Park, California. Generally the results correspond to predictions for the transmission scan-angle coefficient based on a standard atmospheric model although some differences were observed at the longer wavelength channels. A change was detected for the last pair of lines that may indicate either spatial or temporal atmospheric variation. The results demonstrate that the method provides information for correcting regional survey data (requiring multiple adjacent flight lines) that can be important in detecting subtle changes in lithology.

  13. Visual perception enhancement for detection of cancerous oral tissue by multi-spectral imaging

    NASA Astrophysics Data System (ADS)

    Wang, Hsiang-Chen; Tsai, Meng-Tsan; Chiang, Chun-Ping

    2013-05-01

    Color reproduction systems based on the multi-spectral imaging technique (MSI) for both directly estimating reflection spectra and direct visualization of oral tissues using various light sources are proposed. Images from three oral cancer patients were taken as the experimental samples, and spectral differences between pre-cancerous and normal oral mucosal tissues were calculated at three time points during 5-aminolevulinic acid photodynamic therapy (ALA-PDT) to analyze whether they were consistent with disease processes. To check the successful treatment of oral cancer with ALA-PDT, oral cavity images by swept source optical coherence tomography (SS-OCT) are demonstrated. This system can also reproduce images under different light sources. For pre-cancerous detection, the oral images after the second ALA-PDT are assigned as the target samples. By using RGB LEDs with various correlated color temperatures (CCTs) for color difference comparison, the light source with a CCT of about 4500 K was found to have the best ability to enhance the color difference between pre-cancerous and normal oral mucosal tissues in the oral cavity. Compared with the fluorescent lighting commonly used today, the color difference can be improved by 39.2% from 16.5270 to 23.0023. Hence, this light source and spectral analysis increase the efficiency of the medical diagnosis of oral cancer and aid patients in receiving early treatment.

  14. Robust parametric estimation over optimal support of fluid flow structure in multispectral image sequences

    NASA Astrophysics Data System (ADS)

    Rougon, Nicolas F.; Brossard-Pailleux, M. A.; Preteux, Francoise J.

    2000-10-01

    This article presents a methodology for analyzing the Lagrangian structure of fluid flows generated by the evolution of cloud systems in meteorological multispectral image sequences. The correlation between the orientation of cloud texture and the underlying motion field Lagrangian component allows to adopt a static strategy. Following a scale-space approach, we therefore first construct a non-local robust estimator for the locally dominant orientation field in an image. This estimator, which is derived from the image structure tensor, is relevant in both mono- and multisprectral contexts. In a second step, the Lagrangian component of the flow is estimated over some bounded image region by robustly fitting a hierarchical vector parametric model to the dominant orientation field. Here, a recurrent problem deals with adaptating the geometry of the model support to obtain unbiased estimates. To tackle this classic issue, we introduce a novel variational, semi-parametric approach which allows the joint optimization of model parameters and support. This approach is generic and, in particular, can be readily applied to motion estimation yielding robust measurement of the Eulerian structure of the flow. Finally, a structural characterization of the reflecting vector field is derived by means of classic differential geometry techniques. This methodology is applied to the analysis of temperated latitude depressions in Meteosat images.

  15. Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

    USGS Publications Warehouse

    Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

    2002-01-01

    Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.

  16. Multispectral photography for earth resources

    NASA Technical Reports Server (NTRS)

    Wenderoth, S.; Yost, E.; Kalia, R.; Anderson, R.

    1972-01-01

    A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning.

  17. Landsat 8 Multispectral and Pansharpened Imagery Processing on the Study of Civil Engineering Issues

    NASA Astrophysics Data System (ADS)

    Lazaridou, M. A.; Karagianni, A. Ch.

    2016-06-01

    Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM - Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion - pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

  18. Retrieval and classification of food images.

    PubMed

    Farinella, Giovanni Maria; Allegra, Dario; Moltisanti, Marco; Stanco, Filippo; Battiato, Sebastiano

    2016-10-01

    Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification. PMID:27498058

  19. Retrieval and classification of food images.

    PubMed

    Farinella, Giovanni Maria; Allegra, Dario; Moltisanti, Marco; Stanco, Filippo; Battiato, Sebastiano

    2016-10-01

    Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification.

  20. Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images

    PubMed Central

    Lin, Chinsu; Popescu, Sorin C.; Thomson, Gavin; Tsogt, Khongor; Chang, Chein-I

    2015-01-01

    This paper proposes a supervised classification scheme to identify 40 tree species (2 coniferous, 38 broadleaf) belonging to 22 families and 36 genera in high spatial resolution QuickBird multispectral images (HMS). Overall kappa coefficient (OKC) and species conditional kappa coefficients (SCKC) were used to evaluate classification performance in training samples and estimate accuracy and uncertainty in test samples. Baseline classification performance using HMS images and vegetation index (VI) images were evaluated with an OKC value of 0.58 and 0.48 respectively, but performance improved significantly (up to 0.99) when used in combination with an HMS spectral-spatial texture image (SpecTex). One of the 40 species had very high conditional kappa coefficient performance (SCKC ≥ 0.95) using 4-band HMS and 5-band VIs images, but, only five species had lower performance (0.68 ≤ SCKC ≤ 0.94) using the SpecTex images. When SpecTex images were combined with a Visible Atmospherically Resistant Index (VARI), there was a significant improvement in performance in the training samples. The same level of improvement could not be replicated in the test samples indicating that a high degree of uncertainty exists in species classification accuracy which may be due to individual tree crown density, leaf greenness (inter-canopy gaps), and noise in the background environment (intra-canopy gaps). These factors increase uncertainty in the spectral texture features and therefore represent potential problems when using pixel-based classification techniques for multi-species classification. PMID:25978466

  1. Design of a novel multi-spectral imaging spectrometer for breast cancer detector based on VHT grating

    NASA Astrophysics Data System (ADS)

    Ren, Zhong; Wu, Yan; Liu, Guodong; Huang, Zhen; Zeng, Lvming

    2011-06-01

    The ultrasonic imaging, X-mammography, CT imaging and MRI can be applied into the breast cancer diagnosis(BCD). But some factors such as the spatial resolution, contrast and price-performance ratio (PPR) limit their applications. So, a novel BCD technology, that is, multi-spectral imaging is adopted into this paper. It can get more information of the breast tumor and higher identity because it combines the advantages of the spectroscopy and the imaging technology. And in this paper, the multi-spectral light source induced the breast cancer imaging detector(BCID) is designed, the spectrum can cover from the UV to NIR. Meanwhile, a custom-built multi-spectral imaging spectrometer (MSIS) is also developed. And, in order to overcome the stray-light of the light-route system and improve the resolution and light-passing efficiency of the system, the novel volume holography transmissive (VHT) grating instead of the plane or concave grating is used as the diffraction grating in this MSIS. Experimental result show that the novel BCD technology is feasible, it can offer not only the spectral information but also the image of the tumor. The spectrum resolution of the MSIS for BCID based on VHT grating can reach 2nm. Compared with the others, this BCID has more compact structure, faster speed, higher PPR and higher resolution and accuracy. Therefore, this BCID has the potential value in the field of the BCD.

  2. Nanodispersion, nonlinear image filtering, and materials classification

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni F.; Lee, Jun S.

    2011-06-01

    Polyethylene terephthalate-alumina nano-composites from two production processes gave rise to materials H and T, further divided into four and, respectively, three classes of belonging. Electron microscope images of the materials had been visually scored by an expert in terms of an index, β, aimed at assessing filler dispersion and distribution. These properties characterize the nano-composite. Herewith a classification algorithm which includes image spatial differentiation and non-linear filtering interlaced with multivariate statistics is applied to the same images of materials Hand T. The classification algorithm depends on a few parameters, which are automatically determined by maximizing a figure of merit in the supervised training stage. The classifier output is a display on the plane of the first two principal components. By regressing the 1st principal component affinely against β a remarkable agreement is found between automated classification and visual scoring of material H. The regression result for materialT is not significant, because the assigned classes reduce from 3 to 2, both by visual and automated scoring. The output from the non-linear image filter can be related to filler dispersion and distribution.

  3. I-vectors for image classification

    NASA Astrophysics Data System (ADS)

    Smith, David C.

    2014-09-01

    Recent state-of-the-art work on speaker recognition and verification uses a simple factor analysis to derive a low-dimensional total variability space" which simultaneously captures speaker and channel variability. This approach simplified earlier work using joint factor analysis to separately model speaker and channel differences. Here we adapt this "i-vector" method to image classification by replacing speakers with image categories, voice cuts with images, and cepstral features with SURF local descriptors, and where the role of channel variability is attributed to differences in image backgrounds or lighting conditions. A Universal Gaussian mixture model (UGMM) is trained (unsupervised) on SURF descriptors extracted from a varied and extensive image corpus. Individual images are modeled by additively perturbing the supervector of stacked means of this UGMM by the product of a low-rank total variability matrix (TVM) and a normally distributed hidden random vector, X. The TVM is learned by applying an EM algorithm to maximize the sum of log-likelihoods of descriptors extracted from training images, where the likelihoods are computed with respect to the GMM obtained by perturbing the UGMM means via the TVM as above, and leaving UGMM covariances unchanged. Finally, the low-dimensional i-vector representation of an image is the expected value of the posterior distribution of X conditioned on the image's descriptors, and is computed via straightforward matrix manipulations involving the TVM and image-specific Baum-Welch statistics. We compare classification rates found with (i) i-vectors (ii) PCA (iii) Discriminant Attribute Projection (the last two trained on Gaussian MAP-adapted supervector image representations), and (iv) replacing the TVM with the matrix of dominant PCA eigenvectors before i-vector extraction.

  4. Optical-mechanical line-scan imaging process - Its information capacity and efficiency. [satellite multispectral sensing systems application

    NASA Technical Reports Server (NTRS)

    Huck, F. O.; Park, S. K.

    1975-01-01

    Optical-mechanical line-scan techniques have been applied to earth satellite multispectral imaging systems. The capability of the imaging system is generally assessed by its information capacity. An approach based on information theory is developed to formulate the capacity of the line-scan process. Included are the effects of blurring of spatial detail, photosensor noise, aliasing, and quantization. The information efficiency is shown to be dependent on sampling rate, system frequency response shape, SNR, and quantization interval.

  5. Multispectral Mueller polarimetric imaging detecting residual cancer and cancer regression after neoadjuvant treatment for colorectal carcinomas

    NASA Astrophysics Data System (ADS)

    Pierangelo, Angelo; Manhas, Sandeep; Benali, Abdelali; Fallet, Clément; Totobenazara, Jean-Laurent; Antonelli, Maria-Rosaria; Novikova, Tatiana; Gayet, Brice; De Martino, Antonello; Validire, Pierre

    2013-04-01

    This work is devoted to a first exploration of Mueller polarimetric imaging for the detection of residual cancer after neoadjuvant treatment for the rectum. Three samples of colorectal carcinomas treated by radiochemotherapy together with one untreated sample are analyzed ex vivo before fixation in formalin by using a multispectral Mueller polarimetric imaging system operated from 500 to 700 nm. The Mueller images, analyzed using the Lu-Chipmann decomposition, show negligible diattenuation and retardation. The nonirradiated rectum exhibits a variation of depolarization with cancer evolution stage. At all wavelengths on irradiated samples, the contrast between the footprint of the initial tumor and surrounding healthy tissue is found to be much smaller for complete tumor regression than when a residual tumor is present, even at volume fractions of the order of 5%. This high sensitivity is attributed to the modification of stromal collagen induced by the cancer. The depolarization contrast between treated cancer and healthy tissue is found to increase monotonously with the volume fraction of residual cancer in the red part of the spectrum. Polarimetric imaging is a promising technique for detecting short-time small residual cancers, which is valuable information for pathological diagnosis and patient management by clinicians.

  6. Analysis of Multispectral Galileo SSI Images of the Conamara Chaos Region, Europa

    NASA Technical Reports Server (NTRS)

    Spaun, N. A.; Phillips, C. B.

    2003-01-01

    Multispectral imaging of Europa s surface by Galileo s Solid State Imaging (SSI) camera has revealed two major surface color units, which appear as white and red-brown regions in enhanced color images of the surface (see figure). The Galileo Near- Infrared Mapping Spectrometer (NIMS) experiment suggests that the whitish material is icy, almost pure water ice, while the spectral signatures of the reddish regions are dominated by a non-ice material. Two endmember models have been proposed for the composition of the non-ice material: magnesium sulfate hydrates [1] and sulfuric acid and its byproducts [2]. There is also debate concerning whether the origin of this non-ice material is exogenic or endogenic [3].Goals: The key questions this work addresses are: 1) Is the non-ice material exogenic or endogenic in origin? 2) Once emplaced, is this non-ice material primarily modified by exogenic or endogenic processes? 3) Is the non-ice material within ridges, bands, chaos, and lenticulae the same non-ice material across all such geological features? 4) Does the distribution of the non-ice material provide any evidence for or against any of the various models for feature formation? 5) To what extent do the effects of scattered light in SSI images change the spectral signatures of geological features?

  7. Remote mapping of river bathymetry from publicly available multispectral image data

    NASA Astrophysics Data System (ADS)

    Legleiter, C. J.

    2011-12-01

    Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel-bed river, using data acquired through a task-oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image-derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio-based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field-surveyed) vs. predicted (image-derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment due to geo-referencing errors and a coarser spatial resolution. On the larger river, NAIP-derived bathymetry was more accurate, with an observed vs. predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected due to limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions.

  8. Automatic Approach to Vhr Satellite Image Classification

    NASA Astrophysics Data System (ADS)

    Kupidura, P.; Osińska-Skotak, K.; Pluto-Kossakowska, J.

    2016-06-01

    In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step

  9. SQL based cardiovascular ultrasound image classification.

    PubMed

    Nandagopalan, S; Suryanarayana, Adiga B; Sudarshan, T S B; Chandrashekar, Dhanalakshmi; Manjunath, C N

    2013-01-01

    This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

  10. Synergetics Framework for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Müller, R.; Cerra, D.; Reinartz, P.

    2013-05-01

    In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics - originally introduced by the physicist H. Haken - is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.

  11. Multichannel hierarchical image classification using multivariate copulas

    NASA Astrophysics Data System (ADS)

    Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane

    2012-03-01

    This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

  12. Synthesis and Testing of Modular Dual-Modality Nanoparticles for Magnetic Resonance and Multispectral Photoacoustic Imaging.