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Sample records for multispectral image classification

  1. Contextual classification of multispectral image data

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Swain, P. H.

    1981-01-01

    A general method is presented for exploiting both spatial and spectral information when classifying multispectral image data. This statistical classification algorithm utilizes the tendency of certain ground cover classes to be more likely to occur in some contexts than others. The theoretical model assumes the two-dimensional array of random observations and a 0-1 loss function, a distribution of the p-context array that is spatially invariant, and class-conditional independence for the observations. The problems that prevent the immediate use of this context classifier are the need for a generally applicable method for making adequate estimates of the context distribution and a reduction in the computational intensivity of the classifier. The former problem is being approached by a method that raises the relative frequency value for each class configuration to a power and uses the result as the context distribution estimate. The second is being approached by searching for a less computationally intensive algorithm.

  2. Contextual classification of multispectral image data

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Swain, P. H.

    1981-01-01

    A general method is presented for exploiting both spatial and spectral information when classifying multispectral image data. This statistical classification algorithm utilizes the tendency of certain ground cover classes to be more likely to occur in some contexts than others. The theoretical model assumes the two-dimensional array of random observations and a 0-1 loss function, a distribution of the p-context array that is spatially invariant, and class-conditional independence for the observations. The problems that prevent the immediate use of this context classifier are the need for a generally applicable method for making adequate estimates of the context distribution and a reduction in the computational intensivity of the classifier. The former problem is being approached by a method that raises the relative frequency value for each class configuration to a power and uses the result as the context distribution estimate. The second is being approached by searching for a less computationally intensive algorithm.

  3. 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

  4. Classification of multispectral images by using Lagrangian support vector machines

    NASA Astrophysics Data System (ADS)

    Zhu, Hongmei; Yang, Xiaojun

    2008-12-01

    Lagragian support vector machine (LSVM) is a linearly convergent Lagrangian, which is obtained by reformulating the quadratic program of a standard linear support vector machine. To investigate the performance of the classifier working on multispectral images with LSVM as optimizer, we devise a new test based on LSVMs for classifying multispectral data in this work. First of all, data are preprocessed. To acquire the optimum bands for image classification, multispectral image is mapped into a two-dimensional feature space to inspect the bands with redundant spectral information. These extracted data acquired through the feature selection is named data group B relative to the original data group A for a purpose of comparison. Then, to classify multiclass problem, binary classification is extended to multiclass classification by pairwise method. Secondly, two groups of data are trained to find models. In this phase, optimal C values are chosen carefully through trials with different values. Then, classifiers based on LSVMs with optimal C values are used to yield optimal separating hyperplane (OSH). Lastly, in prediction phase, the two groups of data are inputted respectively into each classifier for testing. These classifiers include ones with linear kernel and ones with polynomial kernel of degree 2. The results of the experiment reveal that classifiers with LSVMs as an optimizer have excellent performances with both linear kernel and polynomial kernel of degree 2. Bias caused by the differentia of the two groups of data is not obvious.

  5. 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.

  6. 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).

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Tissue classification for laparoscopic image understanding based on multispectral texture analysis.

    PubMed

    Zhang, Yan; Wirkert, Sebastian J; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T; Elson, Daniel S; Maier-Hein, Lena

    2017-01-01

    Intraoperative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study through statistical analysis, we show that (1) multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

  13. Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic.

    PubMed

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

    2010-06-01

    Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.

  14. 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.

  15. 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.

  16. Neutral Networks for Subpixel Classification of Multispectral Images

    NASA Technical Reports Server (NTRS)

    Figueroa, Ricardo R.; Hunt, Shawn D.

    1998-01-01

    In this work, the implementation and use of AVHRR (Advanced Very High Resolution Radiometer) images for subpixel analysis is studied. The work consists of two parts, the first is making training data, the second is using the training data to design a neural net subpixel analyzer. Most work on subpixel analysis has been done with images with more spectral bands. AVHRR images were chosen because of their easy acquisition, and because the five spectral bands allow investigation into the development of training data. The first step in subpixel analysis is the development of training data. This consists of image to be classified, and the classification of each pixel. In order to do the classification, a high spatial resolution image is typically needed in order to manually create a classified image. It is difficult to have both the image of interest, and a high spatial resolution image of the same area taken at the same time. Thus it was studied whether a subsampled image taken from the image of interest could serve as the training data. Statistical work has been done showing the unusefulness of this approach. In the second part of the analysis, a feedforward neural net was trained and used to classify the AVHRR images. Results of these tests, comparing the neural net with typical statistical based schemes are shown.

  17. 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.

  18. 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.

  19. Enhancement of Multispectral Chromosome Image Classification Using Vector Median Filtering

    NASA Astrophysics Data System (ADS)

    Karvelis, Petros S.; Fotiadis, Dimitrios I.

    Multiplex in-situ hybridization (M-FISH) is a combinatorial labeling technique in which each chromosome is labeled with 5 fluors and a DNA stain and is used for chromosome analysis. Although M-FISH facilitates the visual detection of gross anomalies, misclassified pixels and cross-hybridization often makes manual examination difficult and introduces operator bias. The success of the technique largely depends on the accuracy of pixel classification. In this work we study the use of nonlinear Vector Median Filtering (VMF) methods to induce the accuracy of pixel classification. We have evaluated our methodology using a subset of images publicly available and the classifier was trained and tested on non-overlapping chromosome images. An overall accuracy of 74.13% is reported when introducing VMF.

  20. a Two-Step Decision Fusion Strategy: Application to Hyperspectral and Multispectral Images for Urban Classification

    NASA Astrophysics Data System (ADS)

    Ouerghemmi, W.; Le Bris, A.; Chehata, N.; Mallet, C.

    2017-05-01

    Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in this study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related to class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The classification accuracy is improved up to 5 %, with comparison to the best single source classification accuracy.

  1. 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.

  2. Context distribution estimation for contextual classification of multispectral image data

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Swain, P. H.; Vardeman, S. B.

    1980-01-01

    A classification algorithm incorporating contextual information in a general, statistical manner is presented. Methods are investigated for obtaining adequate estimates of the context distribution (a statistical characterization of context) upon which the classification algorithm depends. Finally, a method of estimating optimal algorithm parameters prior to performing preliminary classifications is explored.

  3. 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.

  4. Estimation of context for statistical classification of multispectral image data

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Vardeman, S. B.; Swain, P. H.

    1982-01-01

    Recent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach. This statistical classification algorithm exploits the tendency of certain ground cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context function. An unbiased estimator of the context function is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context-function estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context-function estimation techniques.

  5. [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.

  6. 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).

  7. On-board multispectral classification study

    NASA Technical Reports Server (NTRS)

    Ewalt, D.

    1979-01-01

    The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.

  8. 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.

  9. 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.

  10. Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

    NASA Astrophysics Data System (ADS)

    Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

    2017-01-01

    Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

  11. Classification by Using Multispectral Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Liao, C. T.; Huang, H. H.

    2012-07-01

    Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

  12. Oil spill classification from multi-spectral satellite images: exploring different machine learning techniques

    NASA Astrophysics Data System (ADS)

    Corucci, Linda; Nardelli, Fabio; Cococcioni, Marco

    2010-10-01

    This work describes the potential of oil spill classification from optical satellite images, as investigated by applying different machine learning techniques to a dataset of more than 300 oil spill candidates, which have been detected from multi-spectral satellite sensors during the years 2008 and 2009, over the entire area of the Mediterranean Sea. A set of geometrical and grey level features from Synthetic Aperture Radar (SAR) literature has been extracted from the regions of interest in order to characterize possible oil spills and feed the classification system. Results obtained by applying different machine learning classifiers to the dataset, and the achieved performance are discussed. In particular, as a first approach to oil spill classification, simple statistical classifiers and neural networks were used. Then, a more interpretable fuzzy rule-based classifier was employed, and performance evaluation was refined by exploiting Receiver Operating Characteristic (ROC) analysis. Finally, since oil spill dataset collection happens incrementally, a suitable technique for online classification was proposed, encompassing at the same time cost-oriented classification, in order to allow for a dynamic change of the misclassification costs. This latter goal has been achieved by building an ensemble of cost-oriented, incremental and decremental support vector machines, exploiting the concept of the ROC convex hull.

  13. A Combined Texture-principal Component Image Classification Technique For Landslide Identification Using Airborne Multispectral Imagery

    NASA Astrophysics Data System (ADS)

    Whitworth, M.; Giles, D.; Murphy, W.

    The Jurassic strata of the Cotswolds escarpment of southern central United Kingdom are associated with extensive mass movement activity, including mudslide systems, rotational and translational landslides. These mass movements can pose a significant engineering risk and have been the focus of research into the use of remote sensing techniques as a tool for landslide identification and delineation on clay slopes. The study has utilised a field site on the Cotswold escarpment above the village of Broad- way, Worcestershire, UK. Geomorphological investigation was initially undertaken at the site in order to establish ground control on landslides and other landforms present at the site. Subsequent to this, Airborne Thematic Mapper (ATM) imagery and colour stereo photography were acquired by the UK Natural Environment Research Coun- cil (NERC) for further analysis and interpretation. This paper describes the textu- ral enhancement of the airborne imagery undertaken using both mean euclidean dis- tance (MEUC) and grey level co-occurrence matrix entropy (GLCM) together with a combined texture-principal component based supervised image classification that was adopted as the method for landslide identification. The study highlights the importance of image texture for discriminating mass movements within multispectral imagery and demonstrates that by adopting a combined texture-principal component image classi- fication we have been able to achieve classification accuracy of 84 % with a Kappa statistic of 0.838 for landslide classes. This paper also highlights the potential prob- lems that can be encountered when using high-resolution multispectral imagery, such as the presence of dense variable woodland present within the image, and presents a solution using principal component analysis.

  14. Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery.

    NASA Astrophysics Data System (ADS)

    González, Albano; Mendez, Zebensui; Munoz, Jonathan; Perez, Juan C.; Armas Padilla, Montserrat

    Clouds play an important role in the Earth's climate system, modulating the radiative energy budget. Consequently, a good knowledge of their radiative properties and of the spatial and temporal distribution of cloud cover is necessary. Earth observation satellites provide us with long time-series data on a global scale and they have become essential tools for the continuous monitoring of cloud properties. Thus, for example, ISCCP (International Satellite Cloud Climatology Project), ERBE (Earth Radiation Budget Experiment) or CERES (Clouds and the Earth s Radiant Energy System) projects have provided essential datasets to improve our understanding of the effects of atmospheric cloud radiative forcing on climate. The problem of cloud segmentation and classification from multispectral satellite imagery is considered in this work. Many methods, based on both supervised and unsupervised classi- fication, have been developed previously, but most of them are based on independent pixel processing. In this study, a segmentation algorithm is applied as a first step, in order to get a partition of the original image into a set of meaningful objects. This segmentation is performed through order-invariant watershed algorithms, based on immersion and toboggan approaches. The multi-scale gradient magnitude has been obtained using a multi-resolution morphological operator from spectral data and texture information, computed through fractal and local binary patterns (LBP) methods. To reduce the oversegmentation produced by the watershed technique, a fast region merging is applied, using region dissimilarity functions that takes into account internal and boundary features.Once the objects present in the image have been segmented, they are classified using a multi-threshold classification method based on physical considerations and radiative and texture features. The proposed technique is applied to MSG-SEVIRI multispectral data, including both daylight and nighttime images. This

  15. Identification and classification of cells in multispectral microscopy images of lymph nodes

    NASA Astrophysics Data System (ADS)

    Liu, Xiaomin; Setiadi, Alvernia F.; Alber, Mark S.; Lee, Peter P.; Chen, Danny Z.

    2011-03-01

    Accurate detection and classification of stained cells in microscopy images enable quantitative measurements of cell distributions and spatial structures, and are crucial for developing new analysis tools for medical studies and applications such as cancer diagnosis and treatment. In this paper, we present a learning based approach for identifying different types of cells in multi-spectral microscopy images of tumor-draining lymph nodes (TDLNs) and locating their centroid positions. With our approach, a set of features based on the eigenvalues of the Hessian matrix is constructed for each image pixel to determine whether the local shape is elliptic. The elliptic features are then used together with the intensity-based ring scores as the feature set for the supervised learning method. Using this new feature set, a random forest based classifier is trained from a set of training samples of different cell types. In order to overcome the difficulties of classifying cells with varying stain qualities, sizes, and shapes, we build a large set of prior training data from a variety of tissue sections. To deal with the issue of multiple overlapping cell nuclei in images, we propose to utilize the spikes of the outer medial axis of the cells to detect and detach the touching cells. As a result, the centroid position of each identified cell is pinpointed. The experimental data show that our proposed method achieves higher recognition rates than previous methods, reducing significantly the human interaction effort involved in previous cell classification work.

  16. Computationally efficient target classification in multispectral image data with Deep Neural Networks

    NASA Astrophysics Data System (ADS)

    Cavigelli, Lukas; Bernath, Dominic; Magno, Michele; Benini, Luca

    2016-10-01

    Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that analyzes the data on-site, close to the sensor, and transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks and are also performing exceptionally well on other computer vision tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.

  17. Fourier multispectral imaging.

    PubMed

    Jia, Jie; Ni, Chuan; Sarangan, Andrew; Hirakawa, Keigo

    2015-08-24

    Current multispectral imaging systems use narrowband filters to capture the spectral content of a scene, which necessitates different filters to be designed for each application. In this paper, we demonstrate the concept of Fourier multispectral imaging which uses filters with sinusoidally varying transmittance. We designed and built these filters employing a single-cavity resonance, and made spectral measurements with a multispectral LED array. The measurements show that spectral features such as transmission and absorption peaks are preserved with this technique, which makes it a versatile technique than narrowband filters for a wide range of multispectral imaging applications.

  18. 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

  19. Automated melanoma detection: multispectral imaging and neural network approach for classification.

    PubMed

    Tomatis, Stefano; Bono, Aldo; Bartoli, Cesare; Carrara, Mauro; Lualdi, Manuela; Tragni, Gabrina; Marchesini, Renato

    2003-02-01

    Our aim in the present research is to investigate the diagnostic performance of artificial neural networks (ANNs) applied to multispectral images of cutaneous pigmented skin lesions as well as to compare this approach to a standard traditional linear classification method, such as discriminant function analysis. This study involves a series of 534 patients with 573 cutaneous pigmented lesions (132 melanomas and 441 nonmelanoma lesions). Each lesion was analyzed by a telespectrophotometric system (TS) in vivo, before surgery. The system is able to acquire a set of 17 images at selected wavelengths from 400 to 1040 nm. For each wavelength, five lesion descriptors were extracted, related to the criteria of the ABCD (for asymmetry, border, color, and dimension) clinical guide for melanoma diagnosis. These variables were first reduced in dimension by the use of factor analysis techniques and then used as input data in an ANN. Multivariate discriminant analysis (MDA) was also performed on the same dataset. The whole dataset was split into two independent groups: i.e., train (the first 400 cases, 95 melanomas) and verification set (last 173 cases, 37 melanomas). Factor analysis was able to summarize the data structure into ten variables, accounting for at least 90% of the original parameters variance. After proper training, the ANN was able to classify the population with 80% sensitivity, 72% specificity, and 78% sensitivity, 76% specificity for the train and validation set, respectively. Following ROC analysis, area under curve (AUC) was 0.852 (train) and 0.847 (verify). Sensitivity and specificity values obtained by the standard discriminant analysis classifier resulted in a figure of 80% sensitivity, 60% specificity and 76% sensitivity, 57% specificity for the train and validation set, respectively. AUC for MDA was 0.810 and 0.764 for the train and verify set, respectively. Classification results were significantly different between the two methods both for diagnostic

  20. 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.

  1. 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.

  2. 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.

  3. Multispectral Image Analysis of Hurricane Gilbert

    DTIC Science & Technology

    1989-05-19

    Classification) Multispectral Image Analysis of Hurrican Gilbert (unclassified) 12. PERSONAL AUTHOR(S) Kleespies, Thomas J. (GL/LYS) 13a. TYPE OF REPORT...cloud top height. component, of tle image in the red channel, and similarly for the green and blue channels. Multispectral Muti.pectral image analysis can...However, there seems to be few references to the human range of vision, the selection as to which mllti.pp.tral image analysis of scenes or

  4. Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

    NASA Technical Reports Server (NTRS)

    Salu, Yehuda; Tilton, James

    1993-01-01

    The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

  5. 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.

  6. Multispectral imaging probe

    SciTech Connect

    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.

  7. Multispectral imaging probe

    SciTech Connect

    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.

  8. [Hard and soft classification method of multi-spectral remote sensing image based on adaptive thresholds].

    PubMed

    Hu, Tan-Gao; Xu, Jun-Feng; Zhang, Deng-Rong; Wang, Jie; Zhang, Yu-Zhou

    2013-04-01

    Hard and soft classification techniques are the conventional methods of image classification for satellite data, but they have their own advantages and drawbacks. In order to obtain accurate classification results, we took advantages of both traditional hard classification methods (HCM) and soft classification models (SCM), and developed a new method called the hard and soft classification model (HSCM) based on adaptive threshold calculation. The authors tested the new method in land cover mapping applications. According to the results of confusion matrix, the overall accuracy of HCM, SCM, and HSCM is 71.06%, 67.86%, and 71.10%, respectively. And the kappa coefficient is 60.03%, 56.12%, and 60.07%, respectively. Therefore, the HSCM is better than HCM and SCM. Experimental results proved that the new method can obviously improve the land cover and land use classification accuracy.

  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. Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee.

    PubMed

    Calvini, Rosalba; Amigo, Jose Manuel; Ulrici, Alessandro

    2017-05-15

    Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set). Copyright © 2017 Elsevier B.V. All rights reserved.

  11. 3D and Multispectral Imaging For Subcutaneous Structures Classification And Analysis

    SciTech Connect

    Paquit, Vincent C; Tobin Jr, Kenneth William; Price, Jeffery R; Meriaudeau, Fabrice

    2009-01-01

    A classification method to differentiate subcutaneous structures is presented. To obtain characteristic spectral signatures, we are investigating light propagation phenomena in biological tissues by combining visible to near-infrared multi-wavelength skin imaging and three dimensional topographic imaging of the skin surface.

  12. 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.

  13. Multispectral Internet imaging

    NASA Astrophysics Data System (ADS)

    Brettel, Hans; Schmitt, Francis J. M.

    2000-12-01

    We present a system for multispectral image acquisition which is accessible via an Internet connection. The system includes an electronically tunable spectral filter and a monochrome digital camera, both controlled from a PC-type computer acting as a Web server. In contrast to the three fixed color channels of an ordinary WebCam, our system provides a virtually unlimited number of spectral channels. To allow for interactive use of this multispectral image acquisition system through the network, we developed a set of Java servlets which provide access to the system through HyperText Transfer Protocol (HTTP) requests. Since only the standard Common Gateway Interface (CGI) mechanisms for client-server communication are used, the system is accessible from any Web browser.

  14. 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.

  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. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. Miniature snapshot multispectral imager

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam; Ashe, Philip R.; Tan, Songsheng

    2011-03-01

    We present a miniature snapshot multispectral imager based on using a monolithic filter array that operates in the short wavelength infrared spectral region and has a number of defense and commercial applications. The system is low-weight, portable with a miniature platform, and requires low power. The imager uses a 4×4 Fabry-Pérot filter array operating from 1487 to 1769 nm with a spectral bandpass ~10 nm. The design of the filters is based on using a shadow mask technique to fabricate an array of Fabry-Pérot etalons with two multilayer dielectric mirrors. The filter array is installed in a commercial handheld InGaAs camera, replacing the imaging lens with a custom designed 4×4 microlens assembly with telecentric imaging performance in each of the 16 subimaging channels. We imaged several indoor and outdoor scenes. The microlens assembly and filter design is quite flexible and can be tailored for any wavelength region from the ultraviolet to the longwave infrared, and the spectral bandpass can also be customized to meet sensing requirements. In this paper we discuss the design and characterization of the filter array, the microlens optical assembly, and imager and present imaging results.

  3. Multispectral analysis of multimodal images.

    PubMed

    Kvinnsland, Yngve; Brekke, Njål; Taxt, Torfinn M; Grüner, Renate

    2009-01-01

    An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentations that seem to be sensible. The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

  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.

  5. 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.

  6. 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

  7. Contextual classification of multispectral image data: An unbiased estimator for the context distribution

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Swain, P. H. (Principal Investigator); Vardeman, S. B.

    1981-01-01

    A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

  8. Contextual classification of multispectral image data - An unbiased estimator for the context distribution

    NASA Technical Reports Server (NTRS)

    Tilton, J. C.; Swain, P. H.; Vardeman, S. B.

    1981-01-01

    Recent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach. This statistical classification algorithm exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context distribution. Here a discussion is given of an unbiased estimator of the context distribution which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. Classification of human carcinoma cells using multispectral imagery

    NASA Astrophysics Data System (ADS)

    Ćinar, Umut; Y. Ćetin, Yasemin; Ćetin-Atalay, Rengul; Ćetin, Enis

    2016-03-01

    In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

  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 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.

  16. 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.

  17. Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

    NASA Astrophysics Data System (ADS)

    Heleno, Sandra; Matias, Magda; Pina, Pedro; Sousa, António Jorge

    2016-04-01

    A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

  18. Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

    NASA Astrophysics Data System (ADS)

    Heleno, S.; Matias, M.; Pina, P.; Sousa, A. J.

    2015-09-01

    A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

  19. Image denoising and deblurring using multispectral data

    NASA Astrophysics Data System (ADS)

    Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.

    2017-05-01

    Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.

  20. Multispectral infrared imaging interferometer

    NASA Technical Reports Server (NTRS)

    Potter, A. E., Jr.

    1971-01-01

    Device permitting simultaneous viewing of infrared images at different wavelengths consists of imaging lens, Michelson interferometer, array of infrared detectors, data processing equipment for Fourier transformation of detector signal, and image display unit. Invention is useful in earth resources applications, nondestructive testing, and medical diagnoses.

  1. Merging Panchromatic and Multispectral Images for Enhanced Image Analysis

    DTIC Science & Technology

    1990-08-01

    Multispectral Images for Enhanced Image Analysis I, Curtis K. Munechika grant permission to the Wallace Memorial Library of the Rochester Institute of...0.0 ()0 (.0(%C’ trees 3. 5 2.5% 0.0%l 44. 1% 5 (.()0th ,crass .1 ().W 0.0% 0).0% 97. overall classification accuracy: 87.5%( T-able DlIb . Confusion

  2. 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.

  3. Multispectral and hyperspectral image processing based on the waveform characteristics of spectral remote sensing classification method of large area

    NASA Astrophysics Data System (ADS)

    Peng, Qingqing; Yang, Liao; Shen, Jinxiang

    2011-11-01

    Determination of land cover spatial distribution and change scientifically and accurately have a great significance for monitoring the regional ecological environment and the impact of human activities. This paper proposed a method basing on the waveform characteristics of spectral for mapping large area using TM data simply, quickly and accurately. In the experiments, we segment the image to object and order the object value of the TM5 1~7 except the thermal infrared band and each order result given the only value. In the next, using manual methods assigned the order to get the typical surface feature such as water, soil, vegetation. Classification using spectral waveform characteristics can effectively avoid the TM image classification error, due to spectral differences by accessing time and spatial, spectrum differences on same object and spectral mixing .This method can map the large area of typical surface is simply, fast and accurate, and support on theoretical basis.

  4. Multispectral Imaging Simulation

    NASA Astrophysics Data System (ADS)

    Loefer, Gene R.; Lao, Ken Q.

    1987-09-01

    Current aircraft have a requirement to operate at night and in adverse weather where optical imaging systems are inoperable. Imaging sensors operating at other wavelengths have the potential to provide vision through severe weather, but these systems need to be simulated before assuming the technological and financial risks involved in hardware development. Sensor and atmospheric models have been developed which simulate images at a variety of wavelengths. These models have been incorporated into a modified version of the IVEX Corporation Behold software which is used for the creation of three dimensional views of terrain data bases and includes fractal texturing and anti-aliasing. This new version, called Behold-ms, adds phenomenological models of material properties, such as surface roughness, emissivity, and temperature, and structured atmospheric weather models that consider path emission, backscatter, and specular/diffuse reflections of the sky. To date, images have been simulated in the visible (color), infrared (8-14pm), passive millimeter wave (35 GHz and 95 GHz), and active MMW (35 GHz and 95 GHz). These algorithms can be used for other windows over this spectral range. In order to accommodate the widely varying types of sensed energy while maintaining a practical amount of internal storage, a scheme for scaling each spectral band has been developed. Spatial resolution degradation due to diffraction, which is especially important at millimeter wavelengths, spatial sampling effects, and system noise models are also included. These sensor models and simulations have been used to examine adverse weather landing systems. Simulated images have also been used in image understanding research and spatial superresolution studies.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. Multispectral computational ghost imaging with multiplexed illumination

    NASA Astrophysics Data System (ADS)

    Huang, Jian; Shi, Dongfeng

    2017-07-01

    Computational ghost imaging has attracted wide attention from researchers in many fields over the last two decades. Multispectral imaging as one application of computational ghost imaging possesses spatial and spectral resolving abilities, and is very useful for surveying scenes and extracting detailed information. Existing multispectral imagers mostly utilize narrow band filters or dispersive optical devices to separate light of different wavelengths, and then use multiple bucket detectors or an array detector to record them separately. Here, we propose a novel multispectral ghost imaging method that uses one single bucket detector with multiplexed illumination to produce a colored image. The multiplexed illumination patterns are produced by three binary encoded matrices (corresponding to the red, green and blue colored information, respectively) and random patterns. The results of the simulation and experiment have verified that our method can be effective in recovering the colored object. Multispectral images are produced simultaneously by one single-pixel detector, which significantly reduces the amount of data acquisition.

  10. 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

  11. 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.

  12. Galileo multispectral imaging of Earth

    NASA Astrophysics Data System (ADS)

    Geissler, Paul; Thompson, W. Reid; Greenberg, Richard; Moersch, Jeff; McEwen, Alfred; Sagan, Carl

    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-μm 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 ~2 km/pixel) shows geometrization plausibly attributable to our technical civilization. Water vapor can be uniquely imaged in the Galileo 0.73-μm 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 μm, 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 coverage of the Galileo data set

  13. Spectral Filter Array for Multispectral Imaging

    NASA Astrophysics Data System (ADS)

    Ni, Chuan

    Conventional multispectral imaging (MSI) is achieved by utilizing a spectral filter array -- a set of predetermined narrowband spectral filters spatially multiplexed over an array of pixel sensors, which necessitates different filters to be designed for each application. In this dissertation, we propose a fundamentally different approach to multispectral imaging known as the Fourier Multispectral Imaging (Fourier MSI). The proposed method utilizes broadband multichroic filters with sinusoidally varying transmittance as a function of wavenumber. Unlike narrowband measurements, these sinusoidal filter measurements largely avoid aliasing that contaminates the spectra while undersampling with narrowband filters. Because of this, Fourier MSI provides a better recovery from discrete filter measurements and preserves the spectra features over the entire detecting wavelength range. We designed and fabricated these sinusoidal filters in both bulk and pixel formats, built up multispectral imaging system with the manufactured filters and made spectral imaging measurements with numerous targets like multispectral LED array, color checker, etc. The measurements show that spectral features such as reflection and absorption peaks are well preserved with this technique. Compared to multispectral systems based on narrowband filters, the Fourier MSI system generalizes well to applications where we lack a priori knowledge of the expected spectral content, which makes it a versatile technique for a wide range of multispectral imaging applications.

  14. 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.

  15. Classification of Histology Sections via Multispectral Convolutional Sparse Coding.

    PubMed

    Zhou, Yin; Chang, Hang; Barner, Kenneth; Spellman, Paul; Parvin, Bahram

    2014-06-01

    Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSD-SPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]).

  16. 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.

  17. Single sensor that outputs narrowband multispectral images

    NASA Astrophysics Data System (ADS)

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

    2010-01-01

    We report the work of developing a hand-held (or miniaturized), low-cost, stand-alone, real-time-operation, narrow bandwidth multispectral imaging device for the detection of early stage pressure ulcers.

  18. Simultaneous denoising and compression of multispectral images

    NASA Astrophysics Data System (ADS)

    Hagag, Ahmed; Amin, Mohamed; Abd El-Samie, Fathi E.

    2013-01-01

    A new technique for denoising and compression of multispectral satellite images to remove the effect of noise on the compression process is presented. One type of multispectral images has been considered: Landsat Enhanced Thematic Mapper Plus. The discrete wavelet transform (DWT), the dual-tree DWT, and a simple Huffman coder are used in the compression process. Simulation results show that the proposed technique is more effective than other traditional compression-only techniques.

  19. Multispectral image segmentation of breast pathology

    NASA Astrophysics Data System (ADS)

    Hornak, Joseph P.; Blaakman, Andre; Rubens, Deborah; Totterman, Saara

    1991-06-01

    The signal intensity in a magnetic resonance image is not only a function of imaging parameters but also of several intrinsic tissue properties. Therefore, unlike other medical imaging modalities, magnetic resonance imaging (MRI) allows the imaging scientist to locate pathology using multispectral image segmentation. Multispectral image segmentation works best when orthogonal spectral regions are employed. In MRI, possible spectral regions are spin density (rho) , spin-lattice relaxation time T1, spin-spin relaxation time T2, and texture for each nucleus type and chemical shift. This study examines the ability of multispectral image segmentation to locate breast pathology using the total hydrogen T1, T2, and (rho) . The preliminary results indicate that our technique can locate cysts and fibroadenoma breast lesions with a minimum number of false-positives and false-negatives. Results, T1, T2, and (rho) algorithms, and segmentation techniques are presented.

  20. Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

    PubMed Central

    Haj-Hassan, Hawraa; Chaddad, Ahmad; Harkouss, Youssef; Desrosiers, Christian; Toews, Matthew; Tanougast, Camel

    2017-01-01

    Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest. PMID:28400990

  1. Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network.

    PubMed

    Haj-Hassan, Hawraa; Chaddad, Ahmad; Harkouss, Youssef; Desrosiers, Christian; Toews, Matthew; Tanougast, Camel

    2017-01-01

    Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  2. Classification Metrics for Improved Atmospheric Correction of Multispectral VNIR Imagery

    PubMed Central

    Richter, Rudolf

    2008-01-01

    Multispectral visible/near-infrared (VNIR) earth observation satellites, e.g., Ikonos, Quickbird, ALOS AVNIR-2, and DMC, usually acquire imagery in a few (3 – 5) spectral bands. Atmospheric correction is a challenging task for these images because the standard methods require at least one shortwave infrared band (around 1.6 or 2.2 μm) or hyperspectral instruments to derive the aerosol optical thickness. New classification metrics for defining cloud, cloud over water, haze, water, and saturation are presented to achieve improvements for an automatic processing system. The background is an ESA contract for the development of a prototype atmospheric processor for the optical payload AVNIR-2 on the ALOS platform. PMID:27873911

  3. Toward Multispectral Imaging with Colloidal Metasurface Pixels.

    PubMed

    Stewart, Jon W; Akselrod, Gleb M; Smith, David R; Mikkelsen, Maiken H

    2017-02-01

    Multispectral colloidal metasurfaces are fabricated that exhibit greater than 85% absorption and ≈100 nm linewidths by patterning film-coupled nanocubes in pixels using a fusion of bottom-up and top-down fabrication techniques over wafer-scale areas. With this technique, the authors realize a multispectral pixel array consisting of six resonances between 580 and 1125 nm and reconstruct an RGB image with 9261 color combinations.

  4. Multispectral imaging fluorescence microscopy for living cells.

    PubMed

    Hiraoka, Yasushi; Shimi, Takeshi; Haraguchi, Tokuko

    2002-10-01

    Multispectral imaging technologies have been widely used in fields of astronomy and remote sensing. Interdisciplinary approaches developed in, for example, the National Aeronautics and Space Administration (NASA, USA), the Jet Propulsion Laboratory (JPL, USA), or the Communications Research Laboratory (CRL, Japan) have extended the application areas of these technologies from planetary systems to cellular systems. Here we overview multispectral imaging systems that have been devised for microscope applications. We introduce these systems with particular interest in live cell imaging. Finally we demonstrate examples of spectral imaging of living cells using commercially available systems with no need for user engineering.

  5. Estimating atmospheric parameters and reducing noise for multispectral imaging

    DOEpatents

    Conger, James Lynn

    2014-02-25

    A method and system for estimating atmospheric radiance and transmittance. An atmospheric estimation system is divided into a first phase and a second phase. The first phase inputs an observed multispectral image and an initial estimate of the atmospheric radiance and transmittance for each spectral band and calculates the atmospheric radiance and transmittance for each spectral band, which can be used to generate a "corrected" multispectral image that is an estimate of the surface multispectral image. The second phase inputs the observed multispectral image and the surface multispectral image that was generated by the first phase and removes noise from the surface multispectral image by smoothing out change in average deviations of temperatures.

  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 novel method to detect shadows on multispectral images

    NASA Astrophysics Data System (ADS)

    Daǧlayan Sevim, Hazan; Yardımcı ćetin, Yasemin; Özışık Başkurt, Didem

    2016-10-01

    Shadowing occurs when the direct light coming from a light source is obstructed by high human made structures, mountains or clouds. Since shadow regions are illuminated only by scattered light, true spectral properties of the objects are not observed in such regions. Therefore, many object classification and change detection problems utilize shadow detection as a preprocessing step. Besides, shadows are useful for obtaining 3D information of the objects such as estimating the height of buildings. With pervasiveness of remote sensing images, shadow detection is ever more important. This study aims to develop a shadow detection method on multispectral images based on the transformation of C1C2C3 space and contribution of NIR bands. The proposed method is tested on Worldview-2 images covering Ankara, Turkey at different times. The new index is used on these 8-band multispectral images with two NIR bands. The method is compared with methods in the literature.

  8. 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

  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. 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

  11. Evaluating intensity normalization for multispectral classification of carotid atherosclerotic plaque

    NASA Astrophysics Data System (ADS)

    Gao, Shan; van't Klooster, Ronald; van Wijk, Diederik F.; Nederveen, Aart J.; Lelieveldt, Boudewijn P. F.; van der Geest, Rob J.

    2015-03-01

    Intensity normalization is an important preprocessing step for automatic plaque analysis in MR images as most segmentation algorithms require the images to have a standardized intensity range. In this study, we derived several intensity normalization approaches with inspiration from expert manual analysis protocols, for classification of carotid vessel wall plaque from in vivo multispectral MRI. We investigated intensity normalization based on a circular region centered at lumen (nCircle); based on sternocleidomastoid muscle (nSCM); based on intensity scaling (nScaling); based on manually classified fibrous tissue (nManuFibrous) and based on automatic classified fibrous tissue (nAutoFibrous). The proposed normalization methods were evaluated using three metrics: (1) Dice similarity coefficient (DSC) between manual and automatic segmentation obtained by classifiers using different normalizations; (2) correlation between proposed normalizations and normalization used by expert; (3) Mahalanobis Distance between pairs of components. In the performed classification experiments, features of normalized image, smoothed, gradient magnitude and Laplacian images at multi-scales, distance to lumen, distance to outer wall, wall thickness were calculated for each vessel wall (VW) pixel. A supervised pattern recognition system, based on a linear discriminate classifier, was trained using the manual segmentation result to classify each VW pixel to be one of the four classes: fibrous tissue, lipid, calcification, and loose matrix according to the highest posterior probability. We evaluated our method on image data of 23 patients. Compared to the result of conventional square region based intensity normalizatio n, nScaling resulted in significant increase in DSC for lipid (p = 0.006) and nAutoFibrous resulted in significant increase in DSC for calcification (p = 0.004). In conclusion, it was demonstrated that the conventional region based normalization approach is not optimal and n

  12. Multispectral imaging using a single bucket detector.

    PubMed

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

    2016-04-22

    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.

  13. 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.

  14. 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

  15. Enhancement of LANDSAT imagery by combination of multispectral classification and principal component analysis. [in France

    NASA Technical Reports Server (NTRS)

    Fontanel, A.; Blanchet, C.; Lallemand, C.

    1975-01-01

    Digital enhancement of LANDSAT imagery was obtained by application of principal component analysis separately on each of the classes previously determined in a multispectral classification step. Each part of the image is thus enhanced whatever its spectral signature may be. A document was obtained which is a synthesis between a conventional image and an ordinary computerized classification. The interpreter can, at the same time, take into account not only the classification but also other features such as context and structure. An example is discussed with the help of geological interpretation.

  16. 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.

  17. IMAGE 100: The interactive multispectral image processing system

    NASA Technical Reports Server (NTRS)

    Schaller, E. S.; Towles, R. W.

    1975-01-01

    The need for rapid, cost-effective extraction of useful information from vast quantities of multispectral imagery available from aircraft or spacecraft has resulted in the design, implementation and application of a state-of-the-art processing system known as IMAGE 100. Operating on the general principle that all objects or materials possess unique spectral characteristics or signatures, the system uses this signature uniqueness to identify similar features in an image by simultaneously analyzing signatures in multiple frequency bands. Pseudo-colors, or themes, are assigned to features having identical spectral characteristics. These themes are displayed on a color CRT, and may be recorded on tape, film, or other media. The system was designed to incorporate key features such as interactive operation, user-oriented displays and controls, and rapid-response machine processing. Owing to these features, the user can readily control and/or modify the analysis process based on his knowledge of the input imagery. Effective use can be made of conventional photographic interpretation skills and state-of-the-art machine analysis techniques in the extraction of useful information from multispectral imagery. This approach results in highly accurate multitheme classification of imagery in seconds or minutes rather than the hours often involved in processing using other means.

  18. Intelligent multi-spectral IR image segmentation

    NASA Astrophysics Data System (ADS)

    Lu, Thomas; Luong, Andrew; Heim, Stephen; Patel, Maharshi; Chen, Kang; Chao, Tien-Hsin; Chow, Edward; Torres, Gilbert

    2017-05-01

    This article presents a neural network based multi-spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. This article compares the neural network based segmentation method to the Wavelet-threshold and Grab-Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-spectral IR images.

  19. 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.; ...

    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

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

    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. Our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  2. An unsupervised classification technique for multispectral remote sensing data.

    NASA Technical Reports Server (NTRS)

    Su, M. Y.; Cummings, R. E.

    1973-01-01

    Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a 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. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

  3. 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

  4. Spectral band optimization for multispectral fluorescence imaging

    NASA Astrophysics Data System (ADS)

    Waterhouse, Dale J.; Luthman, A. Siri; Bohndiek, Sarah E.

    2017-02-01

    Multispectral imaging has the potential to improve sensitivity and specificity in biomedical imaging through simultaneous acquisition of both morphological (spatial) and chemical (spectral) information. Performing multispectral imaging in real time with spectrally resolved detector arrays (SRDAs), for example in endoscopy or intraoperative imaging, requires a direct trade off between spatial and spectral resolution. We sought to quantitatively assess the impact of spectral band selection on contrast agent detection in fluorescence endoscopic imaging. As a proof of concept, we measured the `ground truth' spectra from a dilution series of a single near-infrared fluorescent contrast agent using a spectrometer incorporated into the detection path of our endoscope. We then modeled the influence of an SRDA on these spectra and calculated the theoretical endmembers associated with reflectance and fluorescence signals from the pure contrast agent. To test the accuracy of our model, we incorporated into the same endoscope an off-the-shelf SRDA with a 3x3 filter deposition pattern of 9 spectral bands. After spectral unmixing using the modeled endmembers, the amplitude of the fluorescence recorded with the SRDA compared favorably with the amplitude of fluorescence derived from the `ground truth' spectra recorded with the spectrometer. In the future, this approach could be used to minimize the number of spectral bands required in a given imaging system and hence maximize the spatial resolution of the multispectral camera.

  5. Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

    NASA Astrophysics Data System (ADS)

    Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

    2017-06-01

    During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are

  6. Multispectral Imaging in Cultural Heritage Conservation

    NASA Astrophysics Data System (ADS)

    Del Pozo, S.; Rodríguez-Gonzálvez, P.; Sánchez-Aparicio, L. J.; Muñoz-Nieto, A.; Hernández-López, D.; Felipe-García, B.; González-Aguilera, D.

    2017-08-01

    This paper sums up the main contribution derived from the thesis entitled "Multispectral imaging for the analysis of materials and pathologies in civil engineering, constructions and natural spaces" awarded by CIPA-ICOMOS for its connection with the preservation of Cultural Heritage. This thesis is framed within close-range remote sensing approaches by the fusion of sensors operating in the optical domain (visible to shortwave infrared spectrum). In the field of heritage preservation, multispectral imaging is a suitable technique due to its non-destructive nature and its versatility. It combines imaging and spectroscopy to analyse materials and land covers and enables the use of a variety of different geomatic sensors for this purpose. These sensors collect both spatial and spectral information for a given scenario and a specific spectral range, so that, their smaller storage units save the spectral properties of the radiation reflected by the surface of interest. The main goal of this research work is to characterise different construction materials as well as the main pathologies of Cultural Heritage elements by combining active and passive sensors recording data in different ranges. Conclusions about the suitability of each type of sensor and spectral range are drawn in relation to each particular case study and damage. It should be emphasised that results are not limited to images, since 3D intensity data from laser scanners can be integrated with 2D data from passive sensors obtaining high quality products due to the added value that metric brings to multispectral images.

  7. 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.

  8. Classification of multispectral imagery using wavelet transform and dynamic learning neural network

    NASA Astrophysics Data System (ADS)

    Chen, H. C.; Tzeng, Yu-Chang

    1994-12-01

    A recently developed dynamic learning neural network (DL) has been successfully applied to multispectral imagery classification and parameter inversion. For multispectral imagery classification, it is noises and edges such as streets in the urban area and ridges in the mountain area in an image that result in misclassification or unclassification which reduce the classificalion rate. At the image spectrum point of view, noises and edges are the high frequency components in an image. Therefore, edge detection and noise reduction can be done by extracting the high frequency parts from an image to improve the classification rale. Although both noises and edges are the high frequency components, edges represent some userul information while noises should be removed. Thus, edges and noiscs must be separated when the high frequency parts are extracted. The conventional edge detection or noise reduction melhods could not distinguish edges from noises. A new approach, Wavelet transform, is selected to fulfill this requirement. The edge detection and noise reduction pre-processing using Wavelet transform and image classification using dynamic learning neural network are presented in this paper. The experimental results indicate that it did improve the classification rate.1

  9. 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.

  10. Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

    PubMed

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-04-26

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  11. Multispectral LiDAR Data for Land Cover Classification of Urban Areas

    PubMed Central

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-01-01

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. PMID:28445432

  12. 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.

  13. 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.

  14. 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

  15. Measurements and analysis of active/passive multispectral imaging

    NASA Astrophysics Data System (ADS)

    Grönwall, Christina; Hamoir, Dominique; Steinvall, Ove; Larsson, Hâkan; Amselem, Elias; Lutzmann, Peter; Repasi, Endre; Göhler, Benjamin; Barbé, Stéphane; Vaudelin, Olivier; Fracès, Michel; Tanguy, Bernard; Thouin, Emmanuelle

    2013-10-01

    This paper describes a data collection on passive and active imaging and the preliminary analysis. It is part of an ongoing work on active and passive imaging for target identification using different wavelength bands. We focus on data collection at NIR-SWIR wavelengths but we also include the visible and the thermal region. Active imaging in NIRSWIR will support the passive imaging by eliminating shadows during day-time and allow night operation. Among the applications that are most likely for active multispectral imaging, we focus on long range human target identification. We also study the combination of active and passive sensing. The target scenarios of interest include persons carrying different objects and their associated activities. We investigated laser imaging for target detection and classification up to 1 km assuming that another cueing sensor - passive EO and/or radar - is available for target acquisition and detection. Broadband or multispectral operation will reduce the effects of target speckle and atmospheric turbulence. Longer wavelengths will improve performance in low visibility conditions due to haze, clouds and fog. We are currently performing indoor and outdoor tests to further investigate the target/background phenomena that are emphasized in these wavelengths. We also investigate how these effects can be used for target identification and image fusion. Performed field tests and the results of preliminary data analysis are reported.

  16. 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.

  17. 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.

  18. Highly Protable Airborne Multispectral Imaging System

    NASA Technical Reports Server (NTRS)

    Lehnemann, Robert; Mcnamee, Todd

    2001-01-01

    A portable instrumentation system is described that includes and airborne and a ground-based subsytem. It can acquire multispectral image data over swaths of terrain ranging in width from about 1.5 to 1 km. The system was developed especially for use in coastal environments and is well suited for performing remote sensing and general environmental monitoring. It includes a small,munpilotaed, remotely controlled airplance that carries a forward-looking camera for navigation, three downward-looking monochrome video cameras for imaging terrain in three spectral bands, a video transmitter, and a Global Positioning System (GPS) reciever.

  19. Efficient lossless compression scheme for multispectral images

    NASA Astrophysics Data System (ADS)

    Benazza-Benyahia, Amel; Hamdi, Mohamed; Pesquet, Jean-Christophe

    2001-12-01

    Huge amounts of data are generated thanks to the continuous improvement of remote sensing systems. Archiving this tremendous volume of data is a real challenge which requires lossless compression techniques. Furthermore, progressive coding constitutes a desirable feature for telebrowsing. To this purpose, a compact and pyramidal representation of the input image has to be generated. Separable multiresolution decompositions have already been proposed for multicomponent images allowing each band to be decomposed separately. It seems however more appropriate to exploit also the spectral correlations. For hyperspectral images, the solution is to apply a 3D decomposition according to the spatial and to the spectral dimensions. This approach is not appropriate for multispectral images because of the reduced number of spectral bands. In recent works, we have proposed a nonlinear subband decomposition scheme with perfect reconstruction which exploits efficiently both the spatial and the spectral redundancies contained in multispectral images. In this paper, the problem of coding the coefficients of the resulting subband decomposition is addressed. More precisely, we propose an extension to the vector case of Shapiro's embedded zerotrees of wavelet coefficients (V-EZW) with achieves further saving in the bit stream. Simulations carried out on SPOT images indicate the outperformance of the global compression package we performed.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Utility of multispectral imaging in automated quantitative scoring of immunohistochemistry

    PubMed Central

    Fiore, Christopher; Bailey, Dyane; Conlon, Niamh; Wu, Xiaoqiu; Martin, Neil; Fiorentino, Michelangelo; Finn, Stephen; Fall, Katja; Andersson, Swen-Olof; Andren, Ove; Loda, Massimo; Flavin, Richard

    2012-01-01

    Background Automated scanning devices and image analysis software provide a means to overcome the limitations of manual semiquantitative scoring of immunohistochemistry. Common drawbacks to automated imaging systems include an inability to classify tissue type and an inability to segregate cytoplasmic and nuclear staining. Methods Immunohistochemistry for the membranous marker α-catenin, the cytoplasmic marker stathmin and the nuclear marker Ki-67 was performed on tissue microarrays (TMA) of archival formalin-fixed paraffin-embedded tissue comprising 471 (α-catenin and stathmin) and 511 (Ki-67) cases of prostate adenocarcinoma. These TMA were quantitatively analysed using two commercially available automated image analysers, the Ariol SL-50 system and the Nuance system from CRi. Both systems use brightfield microscopy for automated, unbiased and standardised quantification of immunohistochemistry, while the Nuance system has spectral deconvolution capabilities. Results Overall concordance between scores from both systems was excellent (r=0.90; 0.83–0.95). The software associated with the multispectral imager allowed accurate automated classification of tissue type into epithelial glandular structures and stroma, and a single-step segmentation of staining into cytoplasmic or nuclear compartments allowing independent evaluation of these areas. The Nuance system, however, was not able to distinguish reliably between tumour and non-tumour tissue. In addition, variance in the labour and time required for analysis between the two systems was also noted. Conclusion Despite limitations, this study suggests some beneficial role for the use of a multispectral imaging system in automated analysis of immunohistochemistry. PMID:22447914

  5. 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.

  6. Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified 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 CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). 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 (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. 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.

  7. Hyperspectral and multispectral imaging for evaluating food safety and quality

    USDA-ARS?s Scientific Manuscript database

    Spectral imaging technologies have been developed rapidly during the past decade. This paper presents hyperspectral and multispectral imaging technologies in the area of food safety and quality evaluation, with an introduction, demonstration, and summarization of the spectral imaging techniques avai...

  8. 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

    2015-02-17

    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.

  9. 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

  10. Portable Multispectral Colorimeter for Metallic Ion Detection and Classification

    PubMed Central

    Jaimes, Ruth F. V. V.; Borysow, Walter; Gomes, Osmar F.; Salcedo, Walter J.

    2017-01-01

    This work deals with a portable device system applied to detect and classify different metallic ions as proposed and developed, aiming its application for hydrological monitoring systems such as rivers, lakes and groundwater. Considering the system features, a portable colorimetric system was developed by using a multispectral optoelectronic sensor. All the technology of quantification and classification of metallic ions using optoelectronic multispectral sensors was fully integrated in the embedded hardware FPGA ( Field Programmable Gate Array) technology and software based on virtual instrumentation (NI LabView®). The system draws on an indicative colorimeter by using the chromogen reagent of 1-(2-pyridylazo)-2-naphthol (PAN). The results obtained with the signal processing and pattern analysis using the method of the linear discriminant analysis, allows excellent results during detection and classification of Pb(II), Cd(II), Zn(II), Cu(II), Fe(III) and Ni(II) ions, with almost the same level of performance as for those obtained from the Ultravioled and visible (UV-VIS) spectrophotometers of high spectral resolution. PMID:28788082

  11. 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.

  12. Modeling misregistration and related effects on multispectral classification

    NASA Technical Reports Server (NTRS)

    Billingsley, F. C.

    1982-01-01

    Any noise in measurements (due to the scene, sensor, or the analog to digital process) causes a finite fraction of measurements to fall outside of the classification limits. For field boundaries, where the misregistration effects are felt, the misregistration causes the border in a given (set of) band(s) to be closer than expected to a given pixel, so that the mixed materials in the pixels cause additional pixels to fall outside of the class limits. Considerations of the transient distance involved in the difference in brightness between adjacent fields, when scaled to "per pixel", allow the estimation of the width of the border zones. The entire problem is then scaled to field sizes to allow estimation of the global effects. This approach allows the estimation of the accuracy of multispectral classification which might be expected for field interiors, the useful number of quantization bits, and one set of criteria for an unbiased classifier.

  13. A multispectral imaging approach for diagnostics of skin pathologies

    NASA Astrophysics Data System (ADS)

    Lihacova, Ilze; Derjabo, Aleksandrs; Spigulis, Janis

    2013-06-01

    Noninvasive multispectral imaging method was applied for different skin pathology such as nevus, basal cell carcinoma, and melanoma diagnostics. Developed melanoma diagnostic parameter, using three spectral bands (540 nm, 650 nm and 950 nm), was calculated for nevus, melanoma and basal cell carcinoma. Simple multispectral diagnostic device was established and applied for skin assessment. Development and application of multispectral diagnostics method described further in this article.

  14. 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.

  15. An integrated compact airborne multispectral imaging system using embedded computer

    NASA Astrophysics Data System (ADS)

    Zhang, Yuedong; Wang, Li; Zhang, Xuguo

    2015-08-01

    An integrated compact airborne multispectral imaging system using embedded computer based control system was developed for small aircraft multispectral imaging application. The multispectral imaging system integrates CMOS camera, filter wheel with eight filters, two-axis stabilized platform, miniature POS (position and orientation system) and embedded computer. The embedded computer has excellent universality and expansibility, and has advantages in volume and weight for airborne platform, so it can meet the requirements of control system of the integrated airborne multispectral imaging system. The embedded computer controls the camera parameters setting, filter wheel and stabilized platform working, image and POS data acquisition, and stores the image and data. The airborne multispectral imaging system can connect peripheral device use the ports of the embedded computer, so the system operation and the stored image data management are easy. This airborne multispectral imaging system has advantages of small volume, multi-function, and good expansibility. The imaging experiment results show that this system has potential for multispectral remote sensing in applications such as resource investigation and environmental monitoring.

  16. 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.

  17. Multispectral filter array design without training images

    NASA Astrophysics Data System (ADS)

    Shinoda, Kazuma; Yanagi, Yudai; Hayasaki, Yoshio; Hasegawa, Madoka

    2017-08-01

    Multispectral images (MSIs) have been studied for many applications; however, limitations persist in techniques to capture them due to the complexity of assembling one or more prisms and multiple sensor arrays in order to detect signals. Inspired by the application of color filter arrays to commercial digital RGB cameras, a number of researchers have studied multispectral filter arrays (MSFAs) to solve this problem. Determining the measurement wavelength and pattern of an MSFA is important for improving the quality of the demosaicked image. Some conventional studies for designing MSFAs have used training data and have optimized the measurement wavelengths and the pattern by iteratively minimizing the error between the training data and the demosaicked images. We propose a metric to evaluate an MSFA without MSIs, and optimize the measurement wavelengths and the pattern of the MSFA by minimizing the metric. The proposed metric measures the sampling distance between filters in a spatial-spectral domain and quantifies the dispersion of the sampling points by average nearest-neighbor distance (ANND) under a given arbitrary MSFA. Since the quality of the demosaicked image is assumed to be proportional to the degree of dispersion of the sampling points in the spatial-spectral domain, we optimize the MSFA by minimizing the ANND in a nested simulated annealing process. Experimental results show that the optimized MSFA obtained using our method attained a higher peak signal-to-noise ratio (PSNR) than conventional untrained MSFAs in many cases. In addition, the performance difference between some trained MSFAs and the proposed MSFA was small. We also confirmed the validity of the proposed ANND by a comparison with the mean square error obtained from MSI datasets.

  18. Multispectral filter array design without training images

    NASA Astrophysics Data System (ADS)

    Shinoda, Kazuma; Yanagi, Yudai; Hayasaki, Yoshio; Hasegawa, Madoka

    2017-06-01

    Multispectral images (MSIs) have been studied for many applications; however, limitations persist in techniques to capture them due to the complexity of assembling one or more prisms and multiple sensor arrays in order to detect signals. Inspired by the application of color filter arrays to commercial digital RGB cameras, a number of researchers have studied multispectral filter arrays (MSFAs) to solve this problem. Determining the measurement wavelength and pattern of an MSFA is important for improving the quality of the demosaicked image. Some conventional studies for designing MSFAs have used training data and have optimized the measurement wavelengths and the pattern by iteratively minimizing the error between the training data and the demosaicked images. We propose a metric to evaluate an MSFA without MSIs, and optimize the measurement wavelengths and the pattern of the MSFA by minimizing the metric. The proposed metric measures the sampling distance between filters in a spatial-spectral domain and quantifies the dispersion of the sampling points by average nearest-neighbor distance (ANND) under a given arbitrary MSFA. Since the quality of the demosaicked image is assumed to be proportional to the degree of dispersion of the sampling points in the spatial-spectral domain, we optimize the MSFA by minimizing the ANND in a nested simulated annealing process. Experimental results show that the optimized MSFA obtained using our method attained a higher peak signal-to-noise ratio (PSNR) than conventional untrained MSFAs in many cases. In addition, the performance difference between some trained MSFAs and the proposed MSFA was small. We also confirmed the validity of the proposed ANND by a comparison with the mean square error obtained from MSI datasets.

  19. 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.

  20. 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

  1. Novel instrumentation of multispectral imaging technology for detecting tissue abnormity

    NASA Astrophysics Data System (ADS)

    Yi, Dingrong; Kong, Linghua

    2012-10-01

    Multispectral imaging is becoming a powerful tool in a wide range of biological and clinical studies by adding spectral, spatial and temporal dimensions to visualize tissue abnormity and the underlying biological processes. A conventional spectral imaging system includes two physically separated major components: a band-passing selection device (such as liquid crystal tunable filter and diffraction grating) and a scientific-grade monochromatic camera, and is expensive and bulky. Recently 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 clinical requirement for medical diagnostic imaging applications. However the challenging issue of how to integrate and place the micro filter mosaic chip to the targeting focal plane, i.e., the imaging sensor, of an off-shelf CMOS/CCD camera is not reported anywhere. This paper presents the methods and results of integrating such a miniaturized filter with off-shelf CMOS imaging sensors to produce handheld real-time multispectral imaging devices for the application of early stage pressure ulcer (ESPU) detection. Unlike conventional multispectral imaging devices which are bulky and expensive, the resulting handheld real-time multispectral ESPU detector can produce multiple images at different center wavelengths with a single shot, therefore eliminates the image registration procedure required by traditional multispectral imaging technologies.

  2. Evolving forest fire burn severity classification algorithms for multispectral imagery

    NASA Astrophysics Data System (ADS)

    Brumby, Steven P.; Harvey, Neal R.; Bloch, Jeffrey J.; Theiler, James P.; Perkins, Simon J.; Young, Aaron C.; Szymanski, John J.

    2001-08-01

    Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. Resampling approach for anomaly detection in multispectral images

    SciTech Connect

    Theiler, J. P.; Cai, D.

    2003-01-01

    We propose a novel approach for identifying the 'most unusual' samples in a data set, based on a resampling of data attributes. The resampling produces a 'background class' and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i e, most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make 'negative definitions' (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.

  9. Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit.

    PubMed

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

    2014-01-01

    Multispectral imaging with 19 wavelengths in the range of 405-970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit.

  10. Application of Multispectral Imaging to Determine Quality Attributes and Ripeness Stage in Strawberry Fruit

    PubMed Central

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

    2014-01-01

    Multispectral imaging with 19 wavelengths in the range of 405–970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit. PMID:24505317

  11. PORTABLE MULTISPECTRAL IMAGING INSTRUMENT FOR FOOD INDUSTRY

    USDA-ARS?s Scientific Manuscript database

    The objective of this paper is to design and fabricate a hand-held multispectral instrument for real-time contaminant detection. Specifically, the protocol to develop a portable multispectral instrument including optical sensor design, fabrication, calibration, data collection, analysis and algorith...

  12. Comparison of Hyperspectral and Multispectral Satellites for Forest Alliance Classification in the San Francisco Bay Area

    NASA Astrophysics Data System (ADS)

    Clark, M. L.

    2016-12-01

    The goal of this study was to assess multi-temporal, Hyperspectral Infrared Imager (HyspIRI) satellite imagery for improved forest class mapping relative to multispectral satellites. The study area was the western San Francisco Bay Area, California and forest alliances (e.g., forest communities defined by dominant or co-dominant trees) were defined using the U.S. National Vegetation Classification System. Simulated 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery were processed from image data acquired by NASA's AVIRIS airborne sensor in year 2015, with summer and multi-temporal (spring, summer, fall) data analyzed separately. HyspIRI reflectance was used to generate a suite of hyperspectral metrics that targeted key spectral features related to chemical and structural properties. The Random Forests classifier was applied to the simulated images and overall accuracies (OA) were compared to those from real Landsat 8 images. For each image group, broad land cover (e.g., Needle-leaf Trees, Broad-leaf Trees, Annual agriculture, Herbaceous, Built-up) was classified first, followed by a finer-detail forest alliance classification for pixels mapped as closed-canopy forest. There were 5 needle-leaf tree alliances and 16 broad-leaf tree alliances, including 7 Quercus (oak) alliance types. No forest alliance classification exceeded 50% OA, indicating that there was broad spectral similarity among alliances, most of which were not spectrally pure but rather a mix of tree species. In general, needle-leaf (Pine, Redwood, Douglas Fir) alliances had better class accuracies than broad-leaf alliances (Oaks, Madrone, Bay Laurel, Buckeye, etc). Multi-temporal data classifications all had 5-6% greater OA than with comparable summer data. For simulated data, HyspIRI metrics had 4-5% greater OA than Landsat 8 and Sentinel-2 multispectral imagery and 3-4% greater OA than HyspIRI reflectance. Finally, HyspIRI metrics had 8% greater OA than real Landsat 8 imagery. In conclusion, forest

  13. 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.

  14. 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.

  15. Multispectral Image Capturing with Foveon Sensors

    NASA Astrophysics Data System (ADS)

    Gehrke, R.; Greiwe, A.

    2013-08-01

    This article describes a specific image quality problem using an UAV and the commercially available multispectral camera Tetracam ADC Lite. The tests were carried out with commercially available UAV Multirotor MR-X 8 performed under normal use and conditions. The ADC Lite shows a remarkable rolling shutter effect caused by the movement and vibrations of the UAV and a slow readout speed of the sensor. Based on these studies the current state of a sensor development is presented, which is composed of two compact cameras with Foveon sensors. These cameras allow to record high quality image data without motion blur or rolling shutter effect. One camera captures the normal colour range; the second camera is modified for the near infrared. The moving parts of both cameras are glued to ensure that a geometric camera calibration is valid over a longer period of time. The success of the gluing procedure has been proven by multiple calibrations. For the matching of the colour- and infrared image the usability of calibrated relative orientation parameters between both cameras were tested. Despite absolutely synchronous triggering of the cameras by an electrical signal, a time delay can be found up to 3/100 s between the images. This time delay in combination with the movement and rotation of the UAV while taking the photos results in a significant error in the previously calibrated relative orientation. These parameters should not be used in further processing. This article concludes with a first result of a 4-channel image and an outlook on the following investigations.

  16. Cloud-based processing of multi-spectral imaging data

    NASA Astrophysics Data System (ADS)

    Bernat, Amir S.; Bolton, Frank J.; Weiser, Reuven; Levitz, David

    2017-03-01

    Multispectral imaging holds great promise as a non-contact tool for the assessment of tissue composition. Performing multi - spectral imaging on a hand held mobile device would allow to bring this technology and with it knowledge to low resource settings to provide a state of the art classification of tissue health. This modality however produces considerably larger data sets than white light imaging and requires preliminary image analysis for it to be used. The data then needs to be analyzed and logged, while not requiring too much of the system resource or a long computation time and battery use by the end point device. Cloud environments were designed to allow offloading of those problems by allowing end point devices (smartphones) to offload computationally hard tasks. For this end we present a method where the a hand held device based around a smartphone captures a multi - spectral dataset in a movie file format (mp4) and compare it to other image format in size, noise and correctness. We present the cloud configuration used for segmenting images to frames where they can later be used for further analysis.

  17. 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.

  18. Color enhancement in multispectral image of human skin

    NASA Astrophysics Data System (ADS)

    Mitsui, Masanori; Murakami, Yuri; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

    2003-07-01

    Multispectral imaging is receiving attention in medical color imaging, as high-fidelity color information can be acquired by the multispectral image capturing. On the other hand, as color enhancement in medical color image is effective for distinguishing lesion from normal part, we apply a new technique for color enhancement using multispectral image to enhance the features contained in a certain spectral band, without changing the average color distribution of original image. In this method, to keep the average color distribution, KL transform is applied to spectral data, and only high-order KL coefficients are amplified in the enhancement. Multispectral images of human skin of bruised arm are captured by 16-band multispectral camera, and the proposed color enhancement is applied. The resultant images are compared with the color images reproduced assuming CIE D65 illuminant (obtained by natural color reproduction technique). As a result, the proposed technique successfully visualizes unclear bruised lesions, which are almost invisible in natural color images. The proposed technique will provide support tool for the diagnosis in dermatology, visual examination in internal medicine, nursing care for preventing bedsore, and so on.

  19. Analysis of multispectral data using an unsupervised classification technique: Application to VAS

    NASA Technical Reports Server (NTRS)

    Szejwach, G.

    1983-01-01

    A statistical classification method based on clustering of multidimensional histograms was applied to several channels of the VAS multispectral imagery. The method automatically discriminates and classifies atmospheric ground features such as cloud types, atmospheric moisture patterns, ocean, or ground. Such a clustering method has the advantage of forming natural data groupings, without a priori classification. Clusters are not limited by straight lines or plane surfaces as is the case in threshold methods. The method was applied to simultaneous full resolution images from channels 8 (11.2 micron), 10 (6.7 micron), and 12 (3.9 micron). Twenty image segments of 64 by 64, 12 image segments of 128 by 128, and 4 image segments of 254 by 254 picture elements were analyzed. In addition, normal VISSR mode images at 1800, 1830, and 2000 GMT were used to identify the classes. The gray levels measured along a scan line and the result of the classification scheme (dashed curves) for the three channels investigated are shown. Each point of the image is affected to a class. Each class is identified by a center of gravity that is represented by a vector in the three dimensional space of gray levels.

  20. Analysis of multispectral data using an unsupervised classification technique: Application to VAS

    NASA Technical Reports Server (NTRS)

    Szejwach, G.

    1983-01-01

    A statistical classification method based on clustering of multidimensional histograms was applied to several channels of the VAS multispectral imagery. The method automatically discriminates and classifies atmospheric ground features such as cloud types, atmospheric moisture patterns, ocean, or ground. Such a clustering method has the advantage of forming natural data groupings, without a priori classification. Clusters are not limited by straight lines or plane surfaces as is the case in threshold methods. The method was applied to simultaneous full resolution images from channels 8 (11.2 micron), 10 (6.7 micron), and 12 (3.9 micron). Twenty image segments of 64 by 64, 12 image segments of 128 by 128, and 4 image segments of 254 by 254 picture elements were analyzed. In addition, normal VISSR mode images at 1800, 1830, and 2000 GMT were used to identify the classes. The gray levels measured along a scan line and the result of the classification scheme (dashed curves) for the three channels investigated are shown. Each point of the image is affected to a class. Each class is identified by a center of gravity that is represented by a vector in the three dimensional space of gray levels.

  1. 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.

  2. Airborne system for multispectral, multiangle polarimetric imaging.

    PubMed

    Bowles, Jeffrey H; Korwan, Daniel R; Montes, Marcos J; Gray, Deric J; Gillis, David B; Lamela, Gia M; Miller, W David

    2015-11-01

    In this paper, we describe the design, fabrication, calibration, and deployment of an airborne multispectral polarimetric imager. The motivation for the development of this instrument was to explore its ability to provide information about water constituents, such as particle size and type. The instrument is based on four 16 MP cameras and uses wire grid polarizers (aligned at 0°, 45°, 90°, and 135°) to provide the separation of the polarization states. A five-position filter wheel provides for four narrow-band spectral filters (435, 550, 625, and 750 nm) and one blocked position for dark-level measurements. When flown, the instrument is mounted on a programmable stage that provides control of the view angles. View angles that range to ±65° from the nadir have been used. Data processing provides a measure of the polarimetric signature as a function of both the view zenith and view azimuth angles. As a validation of our initial results, we compare our measurements, over water, with the output of a Monte Carlo code, both of which show neutral points off the principle plane. The locations of the calculated and measured neutral points are compared. The random error level in the measured degree of linear polarization (8% at 435) is shown to be better than 0.25%.

  3. Multispectral image fusion based on fractal features

    NASA Astrophysics Data System (ADS)

    Tian, Jie; Chen, Jie; Zhang, Chunhua

    2004-01-01

    Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the

  4. [Detecting fire smoke based on the multispectral image].

    PubMed

    Wei, Ying-Zhuo; Zhang, Shao-Wu; Liu, Yan-Wei

    2010-04-01

    Smoke detection is very important for preventing forest-fire in the fire early process. Because the traditional technologies based on video and image processing are easily affected by the background dynamic information, three limitations exist in these technologies, i. e. lower anti-interference ability, higher false detection rate and the fire smoke and water fog being not easily distinguished. A novel detection method for detecting smoke based on the multispectral image was proposed in the present paper. Using the multispectral digital imaging technique, the multispectral image series of fire smoke and water fog were obtained in the band scope of 400 to 720 nm, and the images were divided into bins. The Euclidian distance among the bins was taken as a measurement for showing the difference of spectrogram. After obtaining the spectral feature vectors of dynamic region, the regions of fire smoke and water fog were extracted according to the spectrogram feature difference between target and background. The indoor and outdoor experiments show that the smoke detection method based on multispectral image can be applied to the smoke detection, which can effectively distinguish the fire smoke and water fog. Combined with video image processing method, the multispectral image detection method can also be applied to the forest fire surveillance, reducing the false alarm rate in forest fire detection.

  5. Crop classification using temporal stacks of multispectral satellite imagery

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Chartrand, Rick; Keisler, Ryan; Longbotham, Nathan; Mertes, Carly; Skillman, Samuel W.; Warren, Michael S.

    2017-05-01

    The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

  6. Fourier Spectral Filter Array for Optimal Multispectral Imaging.

    PubMed

    Jia, Jie; Barnard, Kenneth J; Hirakawa, Keigo

    2016-04-01

    Limitations to existing multispectral imaging modalities include speed, cost, range, spatial resolution, and application-specific system designs that lack versatility of the hyperspectral imaging modalities. In this paper, we propose a novel general-purpose single-shot passive multispectral imaging modality. Central to this design is a new type of spectral filter array (SFA) based not on the notion of spatially multiplexing narrowband filters, but instead aimed at enabling single-shot Fourier transform spectroscopy. We refer to this new SFA pattern as Fourier SFA, and we prove that this design solves the problem of optimally sampling the hyperspectral image data.

  7. Research into multispectral TDI-CCD imaging and fusion technology

    NASA Astrophysics Data System (ADS)

    He, Da; Zhou, Jianyong; Liu, Changlin; Chen, Hongbing

    2016-11-01

    A scanning imaging system based on 6144×96 multi-band five-color TDI-CCD was built, which is featuring Real-time imaging capability with high sensitivity and high dynamic range in multi-spectral bands for the same target. In this paper, the respective pixel topology for five TDI-CCD was obtained on the basis of their spatial relationship in five bands. Finally, high resolution gray-scale image and color image reconstruction for the scenic target were achieved by multi-Spectral fusion algorithm.

  8. Multispectral microwave imaging radar for remote sensing applications

    NASA Technical Reports Server (NTRS)

    Larson, R. W.; Rawson, R.; Ausherman, D.; Bryan, L.; Porcello, L.

    1974-01-01

    A multispectral airborne microwave radar imaging system, capable of obtaining four images simultaneously is described. The system has been successfully demonstrated in several experiments and one example of results obtained, fresh water ice, is given. Consideration of the digitization of the imagery is given and an image digitizing system described briefly. Preliminary results of digitization experiments are included.

  9. 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

  10. Textural features for image classification

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.; Dinstein, I.; Shanmugam, K.

    1973-01-01

    Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

  11. Using Image Tour to Explore Multiangle, Multispectral Satellite Image

    NASA Technical Reports Server (NTRS)

    Braverman, Amy; Wegman, Edward J.; Martinez, Wendy; Symanzik, Juergen; Wallet, Brad

    2006-01-01

    This viewgraph presentation reviews the use of Image Tour to explore the multiangle, multispectral satellite imagery. Remote sensing data are spatial arrays of p-dimensional vectors where each component corresponds to one of p variables. Applying the same R(exp p) to R(exp d) projection to all pixels creates new images, which may be easier to analyze than the original because d < p. Image grand tour (IGT) steps through the space of projections, and d=3 outputs a sequence of RGB images, one for each step. In this talk, we apply IGT to multiangle, multispectral data from NASA's MISR instrument. MISR views each pixel in four spectral bands at nine view angles. Multiple views detect photon scattering in different directions and are indicative of physical properties of the scene. IGT allows us to explore MISR's data structure while maintaining spatial context; a key requirement for physical interpretation. We report results highlighting the uniqueness of multiangle data and how IGT can exploit it.

  12. Image Classification Workflow Using Machine Learning Methods

    NASA Astrophysics Data System (ADS)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

    2016-12-01

    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  13. 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

  14. Novel round-robin tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery.

    PubMed

    Tahir, Muhammad Atif; Bouridane, Ahmed

    2006-10-01

    Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.

  15. 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.

  16. Study pollen grains in rice by using multispectral imaging techniques

    NASA Astrophysics Data System (ADS)

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

    2005-03-01

    This paper describes a novel multispectral imaging microscope and its applications in the study of pollen grains in rice. The Imaging instruments can simultaneously record both spectral and spatial information of a sample, which is helpful to study the chemical states and physical properties of the sample by taking advantage of spatial image processing and spectroscopic analysis techniques. A LCTF (liquid crystal tunable filter) device is used for fast wavelength selection in the range of 400nm to 720nm and a cooled two-dimensional monochrome CCD for image detection. In this paper, the image acquisition process, spatial and spectral calibration and spectral imaging analysis methods are detailed. And also a novel method using this multispectral imaging microscope to observe rice pollen grains is reported here. The multispectral images were systematically processed and analyzed by the software. The results illustrated 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. Compared with cytological and histochemical methods reported previously, the method reported here has demonstrated to be more efficient and reliable in the study of chemical states and physical properties in plant cells.

  17. Skin condition measurement by using multispectral imaging system (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Jung, Geunho; Kim, Sungchul; Kim, Jae Gwan

    2017-02-01

    There are a number of commercially available low level light therapy (LLLT) devices in a market, and face whitening or wrinkle reduction is one of targets in LLLT. The facial improvement could be known simply by visual observation of face, but it cannot provide either quantitative data or recognize a subtle change. Clinical diagnostic instruments such as mexameter can provide a quantitative data, but it costs too high for home users. Therefore, we designed a low cost multi-spectral imaging device by adding additional LEDs (470nm, 640nm, white LED, 905nm) to a commercial USB microscope which has two LEDs (395nm, 940nm) as light sources. Among various LLLT skin treatments, we focused on getting melanin and wrinkle information. For melanin index measurements, multi-spectral images of nevus were acquired and melanin index values from color image (conventional method) and from multi-spectral images were compared. The results showed that multi-spectral analysis of melanin index can visualize nevus with a different depth and concentration. A cross section of wrinkle on skin resembles a wedge which can be a source of high frequency components when the skin image is Fourier transformed into a spatial frequency domain map. In that case, the entropy value of the spatial frequency map can represent the frequency distribution which is related with the amount and thickness of wrinkle. Entropy values from multi-spectral images can potentially separate the percentage of thin and shallow wrinkle from thick and deep wrinkle. From the results, we found that this low cost multi-spectral imaging system could be beneficial for home users of LLLT by providing the treatment efficacy in a quantitative way.

  18. 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.

  19. 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.

  20. Multispectral Imaging Science Working Group for Hydrologic Science: Executive summary

    NASA Technical Reports Server (NTRS)

    1982-01-01

    The following working objectives were adopted: (1) define the current state of knowledge concerning the role of multispectral imaging science in hydrology; (2) identify critical areas where gaps in our knowledge limit opportunities for significant improvements in our understanding of the hydrologic processes; (3) evaluate the potential of multispectral imaging sciences as tools to close these gaps in knowledge; and (4) develop guidelines for a series of remote-sensing-based experiments that would help close these gaps in knowledge and, thereby, provide man with the improved scientific base necessary for better utilization of the world's water resource. The resulting documentation is intended to provide guidance for multispectral imaging programs in the hydrologic sciences with special emphasis on the visible and infrared (IR) wavelengths.

  1. Spatial Resolution Characterization for AWiFS Multispectral Images

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

    This viewgraph presentation describes the spatial resolution of the AWiFS multispectral images characterized by an estimation of the Modulation Transfer Function (MTF) at Nyquist frequency. The contents include: 1) MTF Analysis; 2) Target Analysis; 3) "Pulse Target"; 4) "Pulse" Method; 5) Target Images; 6) Bridge Profiles; 7) MTF Calculation; 8) MTF Results; and 9) Results Summary.

  2. Detection of sudden death syndrome using a multispectral imaging sensor

    USDA-ARS?s Scientific Manuscript database

    Sudden death syndrome (SDS), caused by the fungus Fusarium solani f. sp. glycines, is a widespread mid- to late-season disease with distinctive foliar symptoms. This paper reported the development of an image analysis based method to detect SDS using a multispectral image sensor. A hue, saturation a...

  3. Leica ADS40 Sensor for Coastal Multispectral Imaging

    NASA Technical Reports Server (NTRS)

    Craig, John C.

    2007-01-01

    The Leica ADS40 Sensor as it is used for coastal multispectral imaging is presented. The contents include: 1) Project Area Overview; 2) Leica ADS40 Sensor; 3) Focal Plate Arrangements; 4) Trichroid Filter; 5) Gradient Correction; 6) Image Acquisition; 7) Remote Sensing and ADS40; 8) Band comparisons of Satellite and Airborne Sensors; 9) Impervious Surface Extraction; and 10) Impervious Surface Details.

  4. Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy

    PubMed Central

    Park, Jesung; Pande, Paritosh; Shrestha, Sebina; Clubb, Fred; Applegate, Brian E.; Jo, Javier A.

    2011-01-01

    OBJECTIVE To investigate the potential of endogenous multispectral fluorescence lifetime imaging microscopy (FLIM) for biochemical characterization of human coronary atherosclerotic plaques. METHODS Endogenous multispectral FLIM imaging was performed on the lumen of 58 segments of postmortem human coronary artery. The fluorescence was separated into three emission bands targeting the three main arterial endogenous fluorophores (390±20 nm for collagen, 452±22.5 nm for elastin, and 550±20 for lipids). The fluorescence normalized intensity and average lifetime from each emission band was used to classify each pixel of an image as either “High-Collagen”, “High-Lipids” or “Low-Collagen/Lipids” via multiclass Fisher’s linear discriminant analysis. RESULTS Classification of plaques as either “High-Collagen”, “High-Lipids” or “Low-Collagen/Lipids” based on the endogenous multispectral FLIM was achieved with a sensitivity/specificity of 96/98%, 89/99%, and 99/99%, respectively, where histopathology served as the gold standard. CONCLUSION The endogenous multispectral FLIM approach we have taken, which can readily be adapted for in vivo intravascular catheter based imaging, is capable of reliably identifying plaques with high content of either collagen or lipids. PMID:22138141

  5. 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.

  6. 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

  7. Semiconductor laser multi-spectral sensing and imaging.

    PubMed

    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.

  8. 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.

  9. 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.

  10. Multispectral Image Out-of-Focus Deblurring Using Interchannel Correlation.

    PubMed

    Chen, Shu-Jie; Shen, Hui-Liang

    2015-11-01

    Out-of-focus blur occurs frequently in multispectral imaging systems when the camera is well focused at a specific (reference) imaging channel. As the effective focal lengths of the lens are wavelength dependent, the blurriness levels of the images at individual channels are different. This paper proposes a multispectral image deblurring framework to restore out-of-focus spectral images based on the characteristic of interchannel correlation (ICC). The ICC is investigated based on the fact that a high-dimensional color spectrum can be linearly approximated using rather a few number of intrinsic spectra. In the method, the spectral images are classified into an out-of-focus set and a well-focused set via blurriness computation. For each out-of-focus image, a guiding image is derived from the well-focused spectral images and is used as the image prior in the deblurring framework. The out-of-focus blur is modeled as a Gaussian point spread function, which is further employed as the blur kernel prior. The regularization parameters in the image deblurring framework are determined using generalized cross validation, and thus the proposed method does not need any parameter tuning. The experimental results validate that the method performs well on multispectral image deblurring and outperforms the state of the arts.

  11. 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.

  12. A comparative performance study characterizing breast tissue microarrays using standard RGB and multispectral imaging

    NASA Astrophysics Data System (ADS)

    Qi, Xin; Cukierski, William; Foran, David J.

    2010-02-01

    The lack of clear consensus over the utility of multispectral imaging (MSI) for bright-field imaging prompted our team to investigate the benefit of using MSI on breast tissue microarrays (TMA). We have conducted performance studies to compare MSI with standard bright-field imaging in hematoxylin stained breast tissue. The methodology has three components. The first extracts a region of interest using adaptive thresholding and morphological processing. The second performs texture feature extraction from a local binary pattern within each spectral channel and compared to features of co-occurrence matrix and texture feature coding in third component. The third component performs feature selection and classification. For each spectrum, exhaustive feature selection was used to search for the combination of features that yields the best classification accuracy. AdaBoost with a linear perceptron least-square classifier was applied. The spectra carrying the greatest discriminatory power were automatically chosen and a majority vote was used to make the final classification. 92 breast TMA discs were included in the study. Sensitivity of 0.96 and specificity of 0.89 were achieved on the multispectral data, compared with sensitivity of 0.83 and specificity of 0.85 on RGB data. MSI consistently achieved better classification results than those obtained using standard RGB images. While the benefits of MSI for unmixing multi-stained specimens are well documented, this study demonstrated statistically significant improvements in the automated analysis of single stained bright-field images.

  13. Land mine detection using multispectral image fusion

    SciTech Connect

    Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.; Roeske, F.; Donetti, J.G.; Fields, D.J.; Sherwood, R.J.; Schaich, P.C.

    1995-03-29

    Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). 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 separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts. We use a supervised learning pattern recognition approach to detecting the metal and plastic land mines. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the spectral bands add value to the detection system. The most important features from the various sensors are fused using a supervised learning pattern classifier (the probabilistic neural network). We present results of experiments to detect land mines from real data collected from an airborne platform, and evaluate the usefulness of fusing feature information from multiple spectral bands.

  14. 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

  15. 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.

  16. Prediction of coefficients for lossless compression of multispectral images

    NASA Astrophysics Data System (ADS)

    Ruedin, Ana M. C.; Acevedo, Daniel G.

    2005-08-01

    We present a lossless compressor for multispectral Landsat images that exploits interband and intraband correlations. The compressor operates on blocks of 256 x 256 pixels, and performs two kinds of predictions. For bands 1, 2, 3, 4, 5, 6.2 and 7, the compressor performs an integer-to-integer wavelet transform, which is applied to each block separately. The wavelet coefficients that have not yet been encoded are predicted by means of a linear combination of already coded coefficients that belong to the same orientation and spatial location in the same band, and coefficients of the same location from other spectral bands. A fast block classification is performed in order to use the best weights for each landscape. The prediction errors or differences are finally coded with an entropy - based coder. For band 6.1, we do not use wavelet transforms, instead, a median edge detector is applied to predict a pixel, with the information of the neighbouring pixels and the equalized pixel from band 6.2. This technique exploits better the great similarity between histograms of bands 6.1 and 6.2. The prediction differences are finally coded with a context-based entropy coder. The two kinds of predictions used reduce both spatial and spectral correlations, increasing the compression rates. Our compressor has shown to be superior to the lossless compressors Winzip, LOCO-I, PNG and JPEG2000.

  17. 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.

  18. 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.

  19. The Effect of Multispectral Image Fusion Enhancement on Human Efficiency

    DTIC Science & Technology

    2017-03-20

    associated first-responder situation awareness on military installations. Our team’s research and development effort described within focused on several key...visual system but also allows for comparable examination of fusion across its associated problem space of application. 15. SUBJECT TERMS Ideal observer... associated problem space of application. Keywords: Ideal observer analysis, Efficiency, Image fusion, Multispectral imagery, Landolt C Significance The

  20. Development of Handheld Multispectral Imaging For Food Safety Inspection

    USDA-ARS?s Scientific Manuscript database

    The objective of this research was to develop a handheld multispectral instrument for food safety inspection for poultry carcasses. The prototype system developed in this research consisted of a compact dual-band spectral imaging system, Light Emitting diode (LED), and portable computer. The dual-...

  1. Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

    NASA Astrophysics Data System (ADS)

    Roblyer, Darren; Kurachi, Cristina; Stepanek, Vanda; Schwarz, Richard A.; Williams, Michelle D.; El-Naggar, Adel K.; Lee, J. Jack; Gillenwater, Ann M.; Richards-Kortum, Rebecca

    2010-11-01

    Multispectral widefield optical imaging has the potential to improve early detection of oral cancer. The appropriate selection of illumination and collection conditions is required to maximize diagnostic ability. The goals of this study were to (i) evaluate image contrast between oral cancer/precancer and non-neoplastic mucosa for a variety of imaging modalities and illumination/collection conditions, and (ii) use classification algorithms to evaluate and compare the diagnostic utility of these modalities to discriminate cancers and precancers from normal tissue. Narrowband reflectance, autofluorescence, and polarized reflectance images were obtained from 61 patients and 11 normal volunteers. Image contrast was compared to identify modalities and conditions yielding greatest contrast. Image features were extracted and used to train and evaluate classification algorithms to discriminate tissue as non-neoplastic, dysplastic, or cancer; results were compared to histologic diagnosis. Autofluorescence imaging at 405-nm excitation provided the greatest image contrast, and the ratio of red-to-green fluorescence intensity computed from these images provided the best classification of dysplasia/cancer versus non-neoplastic tissue. A sensitivity of 100% and a specificity of 85% were achieved in the validation set. Multispectral widefield images can accurately distinguish neoplastic and non-neoplastic tissue; however, the ability to separate precancerous lesions from cancers with this technique was limited.

  2. 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.

  3. 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.

  4. 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.

  5. Multispectral image processing for environmental monitoring

    NASA Astrophysics Data System (ADS)

    Carlotto, Mark J.; Lazaroff, Mark B.; Brennan, Mark W.

    1993-03-01

    New techniques are described for detecting environmental anomalies and changes using multispectral imagery. Environmental anomalies are areas that do not exhibit normal signatures due to man-made activities and include phenomena such as effluent discharges, smoke plumes, stressed vegetation, and deforestation. A new region-based processing technique is described for detecting these phenomena using Landsat TM imagery. Another algorithm that can detect the appearance or disappearance of environmental phenomena is also described and an example illustrating its use in detecting urban changes using SPOT imagery is presented.

  6. High Speed Method for in Situ Multispectral Image Registration

    SciTech Connect

    Perrine, Kenneth A.; Lamarche, Brian L.; Hopkins, Derek F.; Budge, Scott E.; Opresko, Lee; Wiley, H. S.; Sowa, Marianne B.

    2007-01-29

    Multispectral confocal spinning disk microscopy provides a high resolution method for real-time live cell imaging. However, optical distortions and the physical misalignments introduced by the use of multiple acquisition cameras can obscure spatial information contained in the captured images. In this manuscript, we describe a multispectral method for real-time image registration whereby the image from one camera is warped onto the image from a second camera via a polynomial correction. This method provides a real-time pixel-for-pixel match between images obtained over physically distinct optical paths. Using an in situ calibration method, the polynomial is characterized by a set of coefficients using a least squares solver. Error analysis demonstrates optimal performance results from the use of cubic polynomials. High-speed evaluation of the warp is then performed through forward differencing with fixed-point data types. Image reconstruction errors are reduced through bilinear interpolation. The registration techniques described here allow for successful registration of multispectral images in real-time (exceeding 15 frame/sec) and have a broad applicability to imaging methods requiring pixel matching over multiple data channels.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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%.

  14. Code-excited linear predictive coding of multispectral MR images

    NASA Astrophysics Data System (ADS)

    Hu, Jian-Hong; Wang, Yao; Cahill, Patrick

    1996-02-01

    This paper reports a multispectral code excited linear predictive coding method for the compression of well-registered multispectral MR images. Different linear prediction models and the adaptation schemes have been compared. The method which uses forward adaptive autoregressive (AR) model has proven to achieve a good compromise between performance, complexity and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over non-overlapping square macroblocks. Each macro-block is further divided into several micro-blocks and, the best excitation signals for each microblock are determined through an analysis-by-synthesis procedure. To satisfy the high quality requirement for medical images, the error between the original images and the synthesized ones are further specified using a vector quantizer. The MFCELP method has been applied to 26 sets of clinical MR neuro images (20 slices/set, 3 spectral bands/slice, 256 by 256 pixels/image, 12 bits/pixel). It provides a significant improvement over the discrete cosine transform (DCT) based JPEG method, a wavelet transform based embedded zero-tree wavelet (EZW) coding method, as well as the MSARMA method we developed before.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. Quality assessment of butter cookies applying multispectral imaging

    PubMed Central

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

    2013-01-01

    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. PMID:24804036

  1. 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.

  2. Crop classification using HJ satellite multispectral data in the North China Plain

    NASA Astrophysics Data System (ADS)

    Jia, Kun; Wu, Bingfang; Li, Qiangzi

    2013-01-01

    The HJ satellite constellation is designed for environment and disaster monitoring by the Chinese government. This paper investigates the performance of multitemporal multispectral charge-coupled device (CCD) data on board HJ-1-A and HJ-1-B for crop classification in the North China Plain. Support vector machine classifier is selected for the classification using different combinations of multitemporal HJ multispectral data. The results indicate that multitemporal HJ CCD data could effectively identify wheat fields with an overall classification accuracy of 91.7%. Considering only single temporal data, 88.2% is the best classification accuracy achieved using the data acquired at the flowering time of wheat. The performance of the combination of two temporal data acquired at the jointing and flowering times of wheat is almost as well as using all three temporal data, indicating that two appropriate temporal data are enough for wheat classification, and much more data have little effect on improving the classification accuracy. Moreover, two temporal data acquired over a larger time interval achieves better results than that over a smaller interval. However, the field borders and smaller cotton fields cannot be identified effectively by HJ multispectral data, and misclassification phenomenon exists because of the relatively coarse spatial resolution.

  3. 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

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

    PubMed

    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.

  5. 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.

  6. Iris biometric system design using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Widhianto, Benedictus Yohanes Bagus Y. B.; Nasution, Aulia M. T.

    2016-11-01

    An identity recognition system is a vital component that cannot be separated from life, iris biometric is one of the biometric that has the best accuracy reaching 99%. Usually, iris biometric systems use infrared spectrum lighting to reduce discomfort caused by radiation when the eye is given direct light, while the eumelamin that is forming the iris has the most flourescent radiation when given a spectrum of visible light. This research will be conducted by detecting iris wavelengths of 850 nm, 560 nm, and 590 nm, where the detection algorithm will be using Daugman algorithm by using a Gabor wavelet extraction feature, and matching feature using a Hamming distance. Results generated will be analyzed to identify how much differences there are, and to improve the accuracy of the multispectral biometric system and as a detector of the authenticity of the iris. The results obtained from the analysis of wavelengths 850 nm, 560 nm, and 590 nm respectively has an accuracy of 99,35 , 97,5 , 64,5 with a matching score of 0,26 , 0,23 , 0,37.

  7. Multiview: a novel multispectral IR imaging camera

    NASA Astrophysics Data System (ADS)

    Soel, Michael A.; Rudman, Stanley; Ryan, Robert; Fonneland, Nils J.; Milano, Steve J.

    1997-06-01

    The Surveillance Sciences Directorate of the Northrop Grumman Advanced Systems and Technology organization is developing a novel Multispectral IR camera known as Multiview. This prototype system is capable of simultaneously acquiring 4-color SWIR/MWIR 2D imagery that is both spatially and temporally registered utilizing a single 2562 HgCdTe snapshot IR FPA capable of frame rates in excess of 240 Hz. The patented design offers an extremely compact package that contains the entire optomechanical assembly (lenses, interchangeable filters, and cold shield) in less than a 3 in3 volume. The unique imagery collected with this camera has the potential to significantly improve the effectiveness of clutter suppression algorithms, multi-color target detection and target-background discrimination for a wide variety of mission scenarios. This paper describes the key aspects of the Multiview prototype camera design and operation. Multiview's ability to dynamically manage flux imbalances between the four subbands is discussed. Radiometric performance predictions are presented along with laboratory validation of many of these performance metrics. Several examples of field collected imagery is shown including examples of transient rocket plume data measured at 240 Hz sample rate. The importance and utility of spatio-temporal multi-band imagery is also discussed.

  8. 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

  9. 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.

  10. An improved RANSAC algorithm for line matching on multispectral images

    NASA Astrophysics Data System (ADS)

    Wei, Lijun; Li, Yong; Yu, Hang; Xu, Liangpeng; Fan, Chunxiao

    2017-02-01

    This paper proposes a method for removing mismatched lines on multispectral images. The inaccurate detection of ending points brings a great challenge for matching lines since corresponding lines may not be integrally extracted. Due to the inaccurate detection of ending points, lines are usually mismatched with the line description. To eliminate the mismatched lines, we employ a modified RANSAC (Random Sample Consensus) consisting of two steps: (1) pick three line matches randomly and determine their intersections, which are used to calculate a transformation; (2) the best transformation is obtained by sorting the matching score of line matches and then the inliers are declared as the correct matches. Experimental results show that the proposed method can effectively remove incorrect matches on multispectral images.

  11. Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements.

    PubMed

    Medina, José M; Díaz, José A; Vignolo, Carlos

    2014-07-23

    Sparkle in surface coatings is a property of mirror-like pigment particles that consists of remarkable bright spots over a darker surround under unidirectional illumination. We developed a novel nondestructive method to characterize sparkles based on the multispectral imaging technique, and we focused on automotive metallic coatings containing aluminum flake pigments. Multispectral imaging was done in the visible spectrum at different illumination angles around the test sample. Reflectance spectra at different spatial positions were mapped to color coordinates and visualized in different color spaces. Spectral analysis shows that sparkles exhibit higher reflectance spectra and narrower bandwidths. Colorimetric analysis indicates that sparkles present higher lightness values and are far apart from the bulk of color coordinates spanned by the surround. A box-counting procedure was applied to examine the fractal organization of color coordinates in the CIE 1976 L*a*b* color space. A characteristic noninteger exponent was found at each illumination position. The exponent was independent of the illuminant spectra. Together, these results demonstrate that sparkles are extreme deviations relative to the surround and that their spectral properties can be described as fractal patterns within the color space. Multispectral reflectance imaging provides a powerful, noninvasive method for spectral identification and classification of sparkles from metal flake pigments on the micron scale.

  12. Ultraminiature optical design for multispectral fluorescence imaging endoscopes

    NASA Astrophysics Data System (ADS)

    Tate, Tyler H.; Keenan, Molly; Black, John; Utzinger, Urs; Barton, Jennifer K.

    2017-03-01

    A miniature wide-field multispectral endoscopic imaging system was developed enabling reflectance and fluorescence imaging over a broad wavelength range. At 0.8-mm diameter, the endoscope can be utilized for natural orifice imaging in small lumens such as the fallopian tubes. Five lasers from 250 to 642 nm are coupled into a 125-μm diameter multimode fiber and transmitted to the endoscope distal tip for illumination. Ultraviolet and blue wavelengths excite endogenous fluorophores, which can provide differential fluorescence emission images for health and disease. Visible wavelengths provide reflectance images that can be combined for pseudo-white-light imaging and navigation. Imaging is performed by a 300-μm diameter three-element lens system connected to a 3000-element fiber. The lens system was designed for a 70-deg full field of view, working distance from 3 mm to infinity, and 40% contrast at the Nyquist cutoff of the fiber bundle. Measured performance characteristics are near design goals. The endoscope was utilized to obtain example monochromatic, pseudo-white-light, and composite fluorescence images of phantoms and porcine reproductive tract. This work shows the feasibility of packaging a highly capable multispectral fluorescence imaging system into a miniature endoscopic system that may have applications in early detection of cancer.

  13. 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.

  14. Noninvasive inspection of skin lesions via multispectral imaging

    NASA Astrophysics Data System (ADS)

    Pelagotti, Anna; Ferrara, Pasquale; Pescitelli, Leonardo; Gerlini, Gianni; Piva, Alessandro; Borgognoni, Lorenzo

    2013-04-01

    An optical noninvasive inspection tool is presented to, in vivo, better characterize biological tissues such as human skin. The method proposed exploits a multispectral imaging device to acquire a set of images in the visible and NIR range. This kind of information can be very helpful to improve early diagnosis of melanoma, a very aggressive cutaneous neoplasm, incidence and mortality of which continues to rise worldwide. Currently, noninvasive methods (i.e. dermoscopy) have improved melanoma detection, but the definitive diagnosis is still achieved only by invasive method (istopathological observation of the excised lesion). The multispectral system we developed is capable of imaging layers of structures placed at increasing depth, thanks to the fact that light propagates into the skin and reaches different depths depending on its wavelength. This allows to image many features which are less or not visible in the clinical and dermoscopic examination. A new semeiotics is proposed to describe the content of multispectral images. Dermoscopic criteria can be easily applied to describe each image in the set, however inter-images correlations need new suitable descriptors. The first group of new parameters describes how the dermoscopic features, vary across the set of images. More aspects are then introduced. E.g. the longest wavelength where structures can be detected gives an estimate of the maximum depth reached by the pigmented lesion. While the presence of a bright-to-dark transition between the wavebands in the violet to blue range, reveals the presence of blue-whitish veil, which is a further malignancy marker.

  15. [Design of full-polarized and multi-spectral imaging system based on LCVR].

    PubMed

    Zhang, Ying; Zhao, Hui-jie; Cheng, Xuan; Xiong, Sheng-jun

    2011-05-01

    A new full-polarized multi-spectral imaging system is described, which uses electronically controlled LCVR (liquid crystal variable retarder) to modulate the full-polarized state of light in the visible to IR range. The system consisted of optical lenses, LCVRs, filters and CCD. Firstly, the system structure, working theory and optical design are introduced in the present paper. A polarization calibration method is provided and the calibration system was set up, which realized high-precision polarization calibration using a small polarized source. Then, a field experiment with the imaging system was carried out. Polarized spectral images with higher spectral and spatial resolution were collected. Finally, the data acquired were rough processed to get polarization degree image of the targets. It is concluded that the experiment has proved that the imaging system is effective in obtaining full-polarized and multi- spectral data. The image captured by the system can be applied to object identification and object classification.

  16. An experiment in multispectral, multitemporal crop classification using relaxation techniques

    NASA Technical Reports Server (NTRS)

    Davis, L. S.; Wang, C.-Y.; Xie, H.-C

    1983-01-01

    The paper describes the result of an experimental study concerning the use of probabilistic relaxation for improving pixel classification rates. Two LACIE sites were used in the study and in both cases, relaxation resulted in a marked improvement in classification rates.

  17. 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.

  18. Multispectral Image Enhancement Through Adaptive Wavelet Fusion

    DTIC Science & Technology

    2017-02-08

    maps at successive levels of resolution. Next, at each resolution level binary weighting maps are obtained as the pixelwise maximum of corresponding...source saliency maps . Guided filtering of the binary weighting maps with their corresponding source images as guidance images serves to reduce noise...lowest resolution base layers. Application to multiband visual (intensified) and thermal infrared imagery demonstrates that the proposed method

  19. Adaptive ladar receiver for multispectral imaging

    NASA Astrophysics Data System (ADS)

    Johnson, Kenneth; Vaidyanathan, Mohan; Xue, Song; Tennant, William E.; Kozlowski, Lester J.; Hughes, Gary W.; Smith, Duane D.

    2001-09-01

    We are developing a novel 2D focal plane array (FPA) with read-out integrated circuit (ROIC) on a single chip for 3D laser radar imaging. The ladar will provide high-resolution range and range-resolved intensity images for detection and identification of difficult targets. The initial full imaging-camera-on-a-chip system will be a 64 by 64 element, 100-micrometers pixel-size detector array that is directly bump bonded to a low-noise 64 by 64 array silicon CMOS-based ROIC. The architecture is scalable to 256 by 256 or higher arrays depending on the system application. The system will provide all the required electronic processing at pixel level and the smart FPA enables directly producing the 3D or 4D format data to be captured with a single laser pulse. The detector arrays are made of uncooled InGaAs PIN device for SWIR imaging at 1.5 micrometers wavelength and cooled HgCdTe PIN device for MWIR imaging at 3.8 micrometers wavelength. We are also investigating concepts using multi-color detector arrays for simultaneous imaging at multiple wavelengths that would provide additional spectral dimension capability for enhanced detection and identification of deep-hide targets. The system is suited for flash ladar imaging, for combat identification of ground targets from airborne platforms, flash-ladar imaging seekers, and autonomous robotic/automotive vehicle navigation and collision avoidance applications.

  20. Segmenting clouds from space : a hybrid multispectral classification algorithm for satellite imagery.

    SciTech Connect

    Post, Brian Nelson; Wilson, Mark P.; Smith, Jody Lynn; Wehlburg, Joseph Cornelius; Nandy, Prabal

    2005-07-01

    This paper reports on a novel approach to atmospheric cloud segmentation from a space based multi-spectral pushbroom satellite system. The satellite collects 15 spectral bands ranging from visible, 0.45 um, to long wave infra-red (IR), 10.7um. The images are radiometrically calibrated and have ground sample distances (GSD) of 5 meters for visible to very near IR bands and a GSD of 20 meters for near IR to long wave IR. The algorithm consists of a hybrid-classification system in the sense that supervised and unsupervised networks are used in conjunction. For performance evaluation, a series of numerical comparisons to human derived cloud borders were performed. A set of 33 scenes were selected to represent various climate zones with different land cover from around the world. The algorithm consisted of the following. Band separation was performed to find the band combinations which form significant separation between cloud and background classes. The potential bands are fed into a K-Means clustering algorithm in order to identify areas in the image which have similar centroids. Each cluster is then compared to the cloud and background prototypes using the Jeffries-Matusita distance. A minimum distance is found and each unknown cluster is assigned to their appropriate prototype. A classification rate of 88% was found when using one short wave IR band and one mid-wave IR band. Past investigators have reported segmentation accuracies ranging from 67% to 80%, many of which require human intervention. A sensitivity of 75% and specificity of 90% were reported as well.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. Improved 3D cellular imaging by multispectral focus assessment

    NASA Astrophysics Data System (ADS)

    Zhao, Tong; Xiong, Yizhi; Chung, Alice P.; Wachman, Elliot S.; Farkas, Daniel L.

    2005-03-01

    Biological specimens are three-dimensional structures. However, when capturing their images through a microscope, there is only one plane in the field of view that is in focus, and out-of-focus portions of the specimen affect image quality in the in-focus plane. It is well-established that the microscope"s point spread function (PSF) can be used for blur quantitation, for the restoration of real images. However, this is an ill-posed problem, with no unique solution and with high computational complexity. In this work, instead of estimating and using the PSF, we studied focus quantitation in multi-spectral image sets. A gradient map we designed was used to evaluate the sharpness degree of each pixel, in order to identify blurred areas not to be considered. Experiments with realistic multi-spectral Pap smear images showed that measurement of their sharp gradients can provide depth information roughly comparable to human perception (through a microscope), while avoiding PSF estimation. Spectrum and morphometrics-based statistical analysis for abnormal cell detection can then be implemented in an image database where the axial structure has been refined.

  6. 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.

  7. 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.

  8. Multispectral image fusion for detecting land mines

    SciTech Connect

    Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.; Roeske, F.; Donetti, J.G.; Fields, D.J.; sherwood, R.J.; Schaich, P.C.

    1995-04-01

    This report details a system which fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). 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 separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts.

  9. 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

  10. A multispectral testbed for cardiovascular sensing using imaging photoplethysmography

    NASA Astrophysics Data System (ADS)

    Blackford, Ethan B.; Estepp, Justin R.

    2017-02-01

    Imaging photoplethysmography uses image sensors to measure changes in light absorption resulting from skin microvascular blood volume pulsations throughout the cardiac cycle. Imaging photoplethysmography has been demonstrated as an effective, non-contact means of assessing pulse rate, pulse rate variability, and respiration rate. Other potential uses include measuring spatial blood perfusion, oxygenation, and flow dynamics. Herein we demonstrate the development of a multispectral testbed for imaging photoplethysmography consisting of 12 monochromatic, 120fps imagers with 50nm, bandpass filters distributed from 400-750nm and contained in a 3D-printed, 4x3 grid housing mounted on a tripod positioned orthogonal to the subject. A co-located dual-CCD RGB/near-infrared imager records conventional RGB and NIR images expanding the spectral window recorded. After image registration, a multispectral image cube of the 13, partially overlapping bands is created. A spectrometer records high (spectral) resolution data from the participant's right cheek using a collimating lens attached to the measurement fiber. In addition, a spatial array of 5 RGB imagers placed at 0°, +/-20° and +/-40° positions with respect to the subject is employed for motion and spatial robustness. All imagers are synchronized by a hardware trigger source synchronized with a reference, physiological measurement device recording the subject's electrocardiography, bilateral fingertip and/or ear lobe photoplethysmography, bilateral galvanic skin response, and respiration signals. The development of the testbed and pilot data is presented. A full-scale evaluation of the spectral components of the imaging photoplethysmographic signal, optimization of iPPG SNR, and spatial perfusion and blood flow dynamics is currently underway.

  11. Adaptive on-line classification of multi-spectral scanner data

    NASA Technical Reports Server (NTRS)

    Fromm, F. R.; Northouse, R. A.

    1973-01-01

    A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

  12. Adaptive on-line classification of multi-spectral scanner data

    NASA Technical Reports Server (NTRS)

    Fromm, F. R.; Northouse, R. A.

    1973-01-01

    A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

  13. Camera system for multispectral imaging of documents

    NASA Astrophysics Data System (ADS)

    Christens-Barry, William A.; Boydston, Kenneth; France, Fenella G.; Knox, Keith T.; Easton, Roger L., Jr.; Toth, Michael B.

    2009-02-01

    A spectral imaging system comprising a 39-Mpixel monochrome camera, LED-based narrowband illumination, and acquisition/control software has been designed for investigations of cultural heritage objects. Notable attributes of this system, referred to as EurekaVision, include: streamlined workflow, flexibility, provision of well-structured data and metadata for downstream processing, and illumination that is safer for the artifacts. The system design builds upon experience gained while imaging the Archimedes Palimpsest and has been used in studies of a number of important objects in the LOC collection. This paper describes practical issues that were considered by EurekaVision to address key research questions for the study of fragile and unique cultural objects over a range of spectral bands. The system is intended to capture important digital records for access by researchers, professionals, and the public. The system was first used for spectral imaging of the 1507 world map by Martin Waldseemueller, the first printed map to reference "America." It was also used to image sections of the Carta Marina 1516 map by the same cartographer for comparative purposes. An updated version of the system is now being utilized by the Preservation Research and Testing Division of the Library of Congress.

  14. 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)

  15. 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.

  16. Energy dependence of scatter components in multispectral PET imaging.

    PubMed

    Bentourkia, M; Msaki, P; Cadorette, J; Lecomte, R

    1995-01-01

    High resolution images in PET based on small individual detectors are obtained at the cost of low sensitivity and increased detector scatter. These limitations can be partially overcome by enlarging discrimination windows to include more low-energy events and by developing more efficient energy-dependent methods to correct for scatter radiation from all sources. The feasibility of multispectral scatter correction was assessed by decomposing response functions acquired in multiple energy windows into four basic components: object, collimator and detector scatter, and trues. The shape and intensity of these components are different and energy-dependent. They are shown to contribute to image formation in three ways: useful (true), potentially useful (detector scatter), and undesirable (object and collimator scatter) information to the image over the entire energy range. With the Sherbrooke animal PET system, restoration of detector scatter in every energy window would allow nearly 90% of all detected events to participate in image formation. These observations suggest that multispectral acquisition is a promising solution for increasing sensitivity in high resolution PET. This can be achieved without loss of image quality if energy-dependent methods are made available to preserve useful events as potentially useful events are restored and undesirable events removed.

  17. 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.

  18. Multispectral single-scan lung imaging system: initial feasibility

    NASA Astrophysics Data System (ADS)

    Besson, Guy M.; Crocker, Kenneth E.

    2006-03-01

    This paper describes a system for multi-spectral single-scan lung imaging. The proposed approach relies on a low noise detector sampled at a high rate. The proposed method overcomes limitations of CCD time-and-delay integration slot-scanning systems. The system design and preliminary specifications are described. The results of initial spectral and system simulations in support of system feasibility per the outlined specifications are described. Initial investigations support the potential of the proposed approach to alleviate four shortcomings of the current digital flat-panel approach to chest radiography: (i) by enabling dynamic multi-spectral imaging in a single scan, the approach reduces the time delay between exposures, thus reducing sensitivity to motion; (ii) the approach enables dynamic technique feedback and technique adaptation, eliminating the need for a pre-exposures and reducing the likelihood of poor x-ray techniques in local image areas; (iii) by enabling direct measurement of the scatter field, the proposed method allows further scatter correction resulting in image quality improvements; (iv) finally, full-frame sampling of a digital detector allows imaging of the beam penumbra, thereby reclaiming the detection quantum efficiency loss due to over-collimation in current TDI slot-scanning approach; the resulting DQE potentially exceeds that of flat-panel detectors by a factor up to two.

  19. Evaluation of textural features for multispectral images

    NASA Astrophysics Data System (ADS)

    Bayram, Ulya; Can, Gulcan; Duzgun, Sebnem; Yalabik, Nese

    2011-11-01

    Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

  20. Development of a Miniature Snapshot Multispectral Imager

    DTIC Science & Technology

    2010-09-01

    Martins, J. S.; Wolffenbuttel, R. F.; Correia, J. H. An Array of Fabry – Perot Optical – Channels for Biological Fluids Analysis. Sensors and...applications. The system is low weight and portable with a miniature platform, and requires low power. The imager uses a 4×4 Fabry - Perot filter array...shadow mask technique to fabricate a Fabry - Perot etalon with multilayer dielectric mirrors. The filter array subsystem is installed in a commercial

  1. Tunable filters for multispectral imaging of aeronomical features

    NASA Astrophysics Data System (ADS)

    Goenka, C.; Semeter, J. L.; Noto, J.; Dahlgren, H.; Marshall, R.; Baumgardner, J.; Riccobono, J.; Migliozzi, M.

    2013-10-01

    Multispectral imaging of optical emissions in the Earth's upper atmosphere unravels vital information about dynamic phenomena in the Earth-space environment. Wavelength tunable filters allow us to accomplish this without using filter wheels or multiple imaging setups, but with identifiable caveats and trade-offs. We evaluate one such filter, a liquid crystal Fabry-Perot etalon, as a potential candidate for the next generation of imagers for aeronomy. The tunability of such a filter can be exploited in imaging features such as the 6300-6364 Å oxygen emission doublet, or studying the rotational temperature of N2+ in the 4200-4300 Å range, observations which typically require multiple instruments. We further discuss the use of this filter in an optical instrument, called the Liquid Crystal Hyperspectral Imager (LiCHI), which will be developed to make simultaneous measurements in various wavelength ranges.

  2. The analysis of multispectral image data with self-organizing feature maps

    SciTech Connect

    Schaale, M.

    1996-11-01

    The analysis of multispectral sceneries is still a challenging task although many different algorithms for a mathematical analysis exist. Most classification algorithms work in a supervised mode only, i.e. they need to know the possible land usage classes prior to the calculation. In this step many simplifications and assumptions have to be done which directly influence the result. KOHONEN`s self-organizing feature maps, which are based on a biological model, provide a powerful tool to describe the multispectral scenery under consideration with a limited number of reference vectors, the so-called codebook. The resulting code-book, generated with an unsupervised learning scheme, is a compressed description of the multi-dimensional data in terms of non-linear principal components which thus overcomes the problems of a linear principal component analysis. Using this code-book as a basis for a lateral fully interconnected network and introducing a non-linear activity flow between radial-basis functions located at the-positions of the reference vectors results in an unsupervised clustering scheme. This method has been successfully adapted to multispectral sceneries recorded by casi (compact airborne spectrographic imager). 16 refs., 8 figs., 1 tab.

  3. GRIN optics for multispectral infrared imaging

    NASA Astrophysics Data System (ADS)

    Gibson, Daniel; Bayya, Shyam; Nguyen, Vinh; Sanghera, Jas; Kotov, Mikhail; Drake, Gryphon

    2015-06-01

    Graded index (GRIN) optics offer potential for both weight savings and increased performance but have so far been limited to visible and NIR bands (wavelengths shorter than about 0.9 μm). NRL is developing a capability to extend GRIN optics to longer wavelengths in the infrared by exploiting diffused IR transmitting chalcogenide glasses. These IR-GRIN lenses are compatible with all IR wavebands (SWIR, MWIR and LWIR) and can be used alongside conventional wideband materials. Traditional multiband IR imagers require many elements for correction of chromatic aberrations, making them large and heavy and not well-suited for weight sensitive platforms. IR-GRIN optical elements designed with simultaneous optical power and chromatic correction can reduce the number of elements in wideband systems, making multi-band IR imaging practical for platforms including small UAVs and soldier handheld, helmet or weapon mounted cameras. The IR-GRIN lens technology, design space and anti-reflection considerations are presented in this paper.

  4. Multispectral imaging of tablets in blister packaging.

    PubMed

    Malik, I; Poonacha, M; Moses, J; Lodder, R A

    2001-06-09

    This experiment tested the hypothesis that using near-infrared (IR) imaging spectrometry on tablets through blister packs permits the identification and composition of multiple individual tablets to be determined simultaneously. Aspirin was selected for this study because its breakdown mechanism is well understood. Near-IR cameras were used to collect thousands of spectra simultaneously from a field of packaged aspirin tablets. Tablets were selected by a principal component analysis selection algorithm. Graphs of the columns of the transformation matrix showed that salicylic acid and acetylsalicylic acid in the samples were modeled by the principal components. The bootstrap error-adjusted single-sample technique chemometric-imaging algorithm was used to draw probability-density contour plots that revealed tablet composition. Choice of color was used to represent constituent identity, whereas intensity represented concentration. The percentage of usable pixels in the indium antimonide (InSb) array was 99.9%. The SEP was 0.06% of the tablet mass for both water uptake and salicylic acid production. The number of tablets that a typical near-IR camera can currently analyze simultaneously was also estimated to be approximately 1300.

  5. Imaging of Melanin Disruption in Age-Related Macular Degeneration Using Multispectral Imaging.

    PubMed

    Dugel, Pravin U; Zimmer, Cheryl N

    2016-02-01

    To investigate whether multispectral imaging (MSI) is able to obtain a noninvasive view of melanin disruption associated with age-related macular degeneration (AMD), which could support early diagnosis and potential treatment strategies. A single retinal center, retrospective, observational, image analysis study of MSI images of 43 patients was done to determine the extent of melanin pigment exhibited in association with AMD, based on the Age-Related Eye Disease Study classification and grading scale. Corresponding fundus photos were also graded for 12 of the eyes. Fifty-one of 61 eyes (84%) of 43 patients with AMD were determined to have melanin disruption in their MSI images in at least the central and/or one of four inner ETDRS areas. There was a relationship between severity of disease and the degree of melanin disruption. The sensitivity of fundus photography for melanin pigment as compared to MSI was only 62.5%, with three false-negatives. A direct, noninvasive, unobstructed view of melanin disruption associated with AMD can be observed using MSI. Copyright 2016, SLACK Incorporated.

  6. A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier

    NASA Astrophysics Data System (ADS)

    Maan, Bianca; van der Heijden, Ferdi; Fütterer, Jurgen J.

    2012-02-01

    Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.

  7. 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

  8. Jovian chromophore characteristics from multispectral HST images

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

    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 ϖ0( λ), and we remotely observe the culmination of the radiative transfer as I/ F( λ). In this study, we employed a radiative transfer code to retrieve ϖ0( λ) 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 ϖ0( λ) curves were absorbing in the blue, and ϖ0( λ) increased monotonically to approximately unity as wavelength increased. We found accurate fits to all ϖ0( λ) curves using an empirically derived functional form: ϖ0( λ) = 1 - A exp(- Bλ). The best-fit parameters for the mean ϖ0( λ) curve were A = 25.4 and B = 0.0149 for λ in units of nm. We performed a principal component analysis (PCA) on our ϖ0( λ) results and found that one or two independent chromophores were sufficient to produce the variations in ϖ0( λ). A PCA of I/ F( λ) for the same jovian locations resulted in principal components (PCs) with roughly the same variances as the ϖ0( λ) PCA, but they did not result in a one-to-one mapping of PC amplitudes between the ϖ0( λ) PCA and I/ F( λ) PCA. We suggest that statistical analyses performed on I/ F( λ) image cubes have limited applicability to the characterization of chromophores in the jovian atmosphere due to the sensitivity of I/ F( λ) to horizontal variations in the vertical aerosol distribution.

  9. 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.

  10. Sparse-based multispectral image encryption via ptychography

    NASA Astrophysics Data System (ADS)

    Rawat, Nitin; Shi, Yishi; Kim, Byoungho; Lee, Byung-Geun

    2015-12-01

    Recently, we proposed a model of securing a ptychography-based monochromatic image encryption system via the classical Photon-counting imaging (PCI) technique. In this study, we examine a single-channel multispectral sparse-based photon-counting ptychography imaging (SMPI)-based cryptosystem. A ptychography-based cryptosystem creates a complex object wave field, which can be reconstructed by a series of diffraction intensity patterns through an aperture movement. The PCI sensor records only a few complex Bayer patterned samples that have been utilized in the decryption process. Sparse sensing and nonlinear properties of the classical PCI system, together with the scanning probes, enlarge the key space, and such a combination therefore enhances the system's security. We demonstrate that the sparse samples have adequate information for image decryption, as well as information authentication by means of optical correlation.

  11. 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.

  12. Interactive interface for visualizing and analyzing multispectral solar images

    NASA Astrophysics Data System (ADS)

    Hurlbert, Neal E.; Shine, Richard A.; Tarbell, Theodore D.

    1997-03-01

    We present an interactive software tool for manipulating image data, especially high resolution multi-spectral solar movies and images from several different instruments. This tool contains procedures for distortion removal for ground based solar movies, correlation tracking, image alignments, data compression, 3D FOurier filtering, interactive viewing of space/time slices in movies, and browsing through data cubes. This is a compete public domain package based on X windows and Unix which is currently running on Silicon Graphics and Digital Equipment workstations. These software tools are freely available to the international solar community. Many components are also applicable to image an movie analysis in astrophysics, space physics, and earth sciences. They are available with documentation via our web pages under http://www.space.lockheed.com.

  13. 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

  14. Multispectral, Fluorescent and Photoplethysmographic Imaging for Remote Skin Assessment

    PubMed Central

    Spigulis, Janis

    2017-01-01

    Optical tissue imaging has several advantages over the routine clinical imaging methods, including non-invasiveness (it does not change the structure of tissues), remote operation (it avoids infections) and the ability to quantify the tissue condition by means of specific image parameters. Dermatologists and other skin experts need compact (preferably pocket-size), self-sustaining and easy-to-use imaging devices. The operational principles and designs of ten portable in-vivo skin imaging prototypes developed at the Biophotonics Laboratory of Institute of Atomic Physics and Spectroscopy, University of Latvia during the recent five years are presented in this paper. Four groups of imaging devices are considered. Multi-spectral imagers offer possibilities for distant mapping of specific skin parameters, thus facilitating better diagnostics of skin malformations. Autofluorescence intensity and photobleaching rate imagers show a promising potential for skin tumor identification and margin delineation. Photoplethysmography video-imagers ensure remote detection of cutaneous blood pulsations and can provide real-time information on cardiovascular parameters and anesthesia efficiency. Multimodal skin imagers perform several of the abovementioned functions by taking a number of spectral and video images with the same image sensor. Design details of the developed prototypes and results of clinical tests illustrating their functionality are presented and discussed. PMID:28534815

  15. Whole-body and multispectral photoacoustic imaging of adult zebrafish

    NASA Astrophysics Data System (ADS)

    Huang, Na; Xi, Lei

    2016-10-01

    Zebrafish is a top vertebrate model to study developmental biology and genetics, and it is becoming increasingly popular for studying human diseases due to its high genome similarity to that of humans and the optical transparency in embryonic stages. However, it becomes difficult for pure optical imaging techniques to volumetric visualize the internal organs and structures of wild-type zebrafish in juvenile and adult stages with excellent resolution and penetration depth. Even with the establishment of mutant lines which remain transparent over the life cycle, it is still a challenge for pure optical imaging modalities to image the whole body of adult zebrafish with micro-scale resolution. However, the method called photoacoustic imaging that combines all the advantages of the optical imaging and ultrasonic imaging provides a new way to image the whole body of the zebrafish. In this work, we developed a non-invasive photoacoustic imaging system with optimized near-infrared illumination and cylindrical scanning to image the zebrafish. The lateral and axial resolution yield to 80 μm and 600 μm, respectively. Multispectral strategy with wavelengths from 690 nm to 930 nm was employed to image various organs inside the zebrafish. From the reconstructed images, most major organs and structures inside the body can be precisely imaged. Quantitative and statistical analysis of absorption for organs under illumination with different wavelengths were carried out.

  16. Multispectral illumination and image processing techniques for active millimeter-wave concealed object detection.

    PubMed

    Zhang, Lixiao; Stiens, Johan; Elhawil, Amna; Vounckx, Roger

    2008-12-01

    Active millimeter-wave imaging systems for concealed object detection offer the possibility of much higher image contrast than passive systems, especially in indoor applications. By studying active millimeter-wave images of different test objects derived in the W band, we show that multispectral illumination is critical to the detectability of targets. We also propose to use image change detection techniques, including image differencing, normalized difference vegetation index, and principle component analysis to process the multispectral millimeter-wave images. The results demonstrate that multispectral illumination can significantly reveal the object features hidden by image artifacts and improve the appearance of the objects.

  17. River velocities from sequential multispectral remote sensing images

    NASA Astrophysics Data System (ADS)

    Chen, Wei; Mied, Richard P.

    2013-06-01

    We address the problem of extracting surface velocities from a pair of multispectral remote sensing images over rivers using a new nonlinear multiple-tracer form of the global optimal solution (GOS). The derived velocity field is a valid solution across the image domain to the nonlinear system of equations obtained by minimizing a cost function inferred from the conservation constraint equations for multiple tracers. This is done by deriving an iteration equation for the velocity, based on the multiple-tracer displaced frame difference equations, and a local approximation to the velocity field. The number of velocity equations is greater than the number of velocity components, and thus overly constrain the solution. The iterative technique uses Gauss-Newton and Levenberg-Marquardt methods and our own algorithm of the progressive relaxation of the over-constraint. We demonstrate the nonlinear multiple-tracer GOS technique with sequential multispectral Landsat and ASTER images over a portion of the Potomac River in MD/VA, and derive a dense field of accurate velocity vectors. We compare the GOS river velocities with those from over 12 years of data at four NOAA reference stations, and find good agreement. We discuss how to find the appropriate spatial and temporal resolutions to allow optimization of the technique for specific rivers.

  18. Design and fabrication of sinusoidal spectral filters for multispectral imaging

    NASA Astrophysics Data System (ADS)

    Ni, Chuan; Jia, Jie; Hirakawa, Keigo; Sarangan, Andrew

    2015-08-01

    Multispectral imaging beyond the three RGB colors still remains a challenge, especially in portable inexpensive systems. In this paper, we describe the design and fabrication of broadband multichroic filters that have a sinusoidal transmission spectra to utilize a novel methodology based on the Fourier spectral reconstruction in the frequency domain. Since the spectral filters are posed as an optimal sampling of hyperspectral images, they also allow for the reconstruction of the full spectrum from subsequent demosaicking algorithms. Unlike conventional Color Filter Arrays (CFA) which utilizes absorption dyes embedded in a polymeric material, the sinusoidal multichroic filters require an all-dielectric interference filter design. However, the goal of most dielectric filter designs is to achieve sharp transitions with high-contrast. A smoothly varying sinusoidal transition is more difficult with conventional approaches. However, this can be achieved by trading off the contrast. Following the principles of a simple Fabry-Perot cavity, we have designed and built interference filters from 0.5 sinusoidal periods to 3 sinusoidal periods from 450nm to 900nm spectral range. Also, in order to maintain a uniform period across the entire spectrum, the material must have a very low dispersion. In this design, we have used ZnS as the cavity material. The six filters have been used in a multispectral imaging test bed.

  19. 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.

  20. 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.

  1. 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.

  2. Quantitative analysis of multi-spectral fundus images.

    PubMed

    Styles, I B; Calcagni, A; Claridge, E; Orihuela-Espina, F; Gibson, J M

    2006-08-01

    We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.

  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. Blind multispectral image decomposition by 3D nonnegative tensor factorization.

    PubMed

    Kopriva, Ivica; Cichocki, Andrzej

    2009-07-15

    Alpha-divergence-based nonnegative tensor factorization (NTF) is applied to blind multispectral image (MSI) decomposition. The matrix of spectral profiles and the matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models. NTF preserves local structure in the MSI that is lost as a result of vectorization of the image when nonnegative matrix factorization (NMF)- or independent component analysis (ICA)-based decompositions are used. Moreover, NTF based on the PARAFAC model is unique up to permutation and scale under mild conditions. To achieve this, NMF- and ICA-based factorizations, respectively, require enforcement of sparseness (orthogonality) and statistical independence constraints on the spatial distributions of the materials resident in the MSI, and these conditions do not hold. We demonstrate efficiency of the NTF-based factorization in relation to NMF- and ICA-based factorizations on blind decomposition of the experimental MSI with the known ground truth.

  5. Sub-pixel resolution with the Multispectral Thermal Imager (MTI).

    SciTech Connect

    Decker, Max Louis; Smith, Jody Lynn; Nandy, Prabal

    2003-06-01

    The Multispectral Thermal Imager Satellite (MTI) has been used to test a sub-pixel sampling technique in an effort to obtain higher spatial frequency imagery than that of its original design. The MTI instrument is of particular interest because of its infrared detectors. In this spectral region, the detector size is traditionally the limiting factor in determining the satellite's ground sampling distance (GSD). Additionally, many over-sampling techniques require flexible command and control of the sensor and spacecraft. The MTI sensor is well suited for this task, as it is the only imaging system on the MTI satellite bus. In this super-sampling technique, MTI is maneuvered such that the data are collected at sub-pixel intervals on the ground. The data are then processed using a deconvolution algorithm using in-scene measured point spread functions (PSF) to produce an image with synthetically-boosted GSD.

  6. A practical one-shot multispectral imaging system using a single image sensor.

    PubMed

    Monno, Yusuke; Kikuchi, Sunao; Tanaka, Masayuki; Okutomi, Masatoshi

    2015-10-01

    Single-sensor imaging using the Bayer color filter array (CFA) and demosaicking is well established for current compact and low-cost color digital cameras. An extension from the CFA to a multispectral filter array (MSFA) enables us to acquire a multispectral image in one shot without increased size or cost. However, multispectral demosaicking for the MSFA has been a challenging problem because of very sparse sampling of each spectral band in the MSFA. In this paper, we propose a high-performance multispectral demosaicking algorithm, and at the same time, a novel MSFA pattern that is suitable for our proposed algorithm. Our key idea is the use of the guided filter to interpolate each spectral band. To generate an effective guide image, in our proposed MSFA pattern, we maintain the sampling density of the G -band as high as the Bayer CFA, and we array each spectral band so that an adaptive kernel can be estimated directly from raw MSFA data. Given these two advantages, we effectively generate the guide image from the most densely sampled G -band using the adaptive kernel. In the experiments, we demonstrate that our proposed algorithm with our proposed MSFA pattern outperforms existing algorithms and provides better color fidelity compared with a conventional color imaging system with the Bayer CFA. We also show some real applications using a multispectral camera prototype we built.

  7. 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.

  8. 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.

  9. Pairwise KLT-Based Compression for Multispectral Images

    NASA Astrophysics Data System (ADS)

    Nian, Yongjian; Liu, Yu; Ye, Zhen

    2016-12-01

    This paper presents a pairwise KLT-based compression algorithm for multispectral images. Although the KLT has been widely employed for spectral decorrelation, its complexity is high if it is performed on the global multispectral images. To solve this problem, this paper presented a pairwise KLT for spectral decorrelation, where KLT is only performed on two bands every time. First, KLT is performed on the first two adjacent bands and two principle components are obtained. Secondly, one remainning band and the principal component (PC) with the larger eigenvalue is selected to perform a KLT on this new couple. This procedure is repeated until the last band is reached. Finally, the optimal truncation technique of post-compression rate-distortion optimization is employed for the rate allocation of all the PCs, followed by embedded block coding with optimized truncation to generate the final bit-stream. Experimental results show that the proposed algorithm outperforms the algorithm based on global KLT. Moreover, the pairwise KLT structure can significantly reduce the complexity compared with a global KLT.

  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.

  11. Effective key parameter determination for an automatic approach to land cover classification based on multispectral remote sensing imagery.

    PubMed

    Wang, Yong; Jiang, Dong; Zhuang, Dafang; Huang, Yaohuan; Wang, Wei; Yu, Xinfang

    2013-01-01

    The classification of land cover based on satellite data is important for many areas of scientific research. Unfortunately, some traditional land cover classification methods (e.g. known as supervised classification) are very labor-intensive and subjective because of the required human involvement. Jiang et al. proposed a simple but robust method for land cover classification using a prior classification map and a current multispectral remote sensing image. This new method has proven to be a suitable classification method; however, its drawback is that it is a semi-automatic method because the key parameters cannot be selected automatically. In this study, we propose an approach in which the two key parameters are chosen automatically. The proposed method consists primarily of the following three interdependent parts: the selection procedure for the pure-pixel training-sample dataset, the method to determine the key parameters, and the optimal combination model. In this study, the proposed approach employs both overall accuracy and their Kappa Coefficients (KC), and Time-Consumings (TC, unit: second) in order to select the two key parameters automatically instead of using a test-decision, which avoids subjective bias. A case study of Weichang District of Hebei Province, China, using Landsat-5/TM data of 2010 with 30 m spatial resolution and prior classification map of 2005 recognised as relatively precise data, was conducted to test the performance of this method. The experimental results show that the methodology determining the key parameters uses the portfolio optimisation model and increases the degree of automation of Jiang et al.'s classification method, which may have a wide scope of scientific application.

  12. Effective Key Parameter Determination for an Automatic Approach to Land Cover Classification Based on Multispectral Remote Sensing Imagery

    PubMed Central

    Wang, Yong; Jiang, Dong; Zhuang, Dafang; Huang, Yaohuan; Wang, Wei; Yu, Xinfang

    2013-01-01

    The classification of land cover based on satellite data is important for many areas of scientific research. Unfortunately, some traditional land cover classification methods (e.g. known as supervised classification) are very labor-intensive and subjective because of the required human involvement. Jiang et al. proposed a simple but robust method for land cover classification using a prior classification map and a current multispectral remote sensing image. This new method has proven to be a suitable classification method; however, its drawback is that it is a semi-automatic method because the key parameters cannot be selected automatically. In this study, we propose an approach in which the two key parameters are chosen automatically. The proposed method consists primarily of the following three interdependent parts: the selection procedure for the pure-pixel training-sample dataset, the method to determine the key parameters, and the optimal combination model. In this study, the proposed approach employs both overall accuracy and their Kappa Coefficients (KC), and Time-Consumings (TC, unit: second) in order to select the two key parameters automatically instead of using a test-decision, which avoids subjective bias. A case study of Weichang District of Hebei Province, China, using Landsat-5/TM data of 2010 with 30 m spatial resolution and prior classification map of 2005 recognised as relatively precise data, was conducted to test the performance of this method. The experimental results show that the methodology determining the key parameters uses the portfolio optimisation model and increases the degree of automation of Jiang et al.'s classification method, which may have a wide scope of scientific application. PMID:24204582

  13. Multispectral and hyperspectral imaging with AOTF for object recognition

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam; Dahmani, Rachid

    1999-01-01

    Acousto-optic tunable-filter (AOTF) technology has been used in the design of a no-moving parts, compact, lightweight, field portable, automated, adaptive spectral imaging system when combined with a high sensitivity imaging detector array. Such a system could detect spectral signatures of targets and/or background, which contain polarization information and can be digitally processed by a variety of algorithms. At the Army Research Laboratory, we have developed and used a number of AOTF imaging systems and are also carrying out the development of such imagers at longer wavelengths. We have carried out hyperspectral and multispectral imaging using AOTF systems covering the spectral range from the visible to mid-IR. One of the imager uses a two-cascaded collinear-architecture AOTF cell in the visible-to-near-IR range with a digital Si charge-coupled device camera as the detector. The images obtained with this system showed no color blurring or image shift due to the angular deviation of different colors as a result of diffraction, and the digital images are stored and processed with great ease. The spatial resolution of the filter was evaluated by means of the lines of a target chart. We have also obtained and processed images from another noncollinear visible-to-near-IR AOTF imager with a digital camera, and used hyperspectral image processing software to enhance object recognition in cluttered background. We are presently working on a mid-IR AOTF imaging system that uses a high- performance InSb focal plane array and image acquisition and processing software. We describe our hyperspectral imaging program and present results from our imaging experiments.

  14. 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.

  15. 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

  16. Joint alignment of multispectral images via semidefinite programming

    PubMed Central

    Zheng, Yuanjie; Wang, Yu; Jiao, Wanzhen; Hou, Sujuan; Ren, Yanju; Qin, Maoling; Hou, Dewen; Luo, Chao; Wang, Hong; Gee, James; Zhao, Bojun

    2017-01-01

    In this paper, we introduce a novel feature-point-matching based framework for achieving an optimized joint-alignment of sequential images from multispectral imaging (MSI). It solves a low-rank and semidefinite matrix that stores all pairwise-image feature-mappings by minimizing the total amount of point-to-point matching cost via a convex optimization of a semidefinite programming formulation. This unique strategy takes a complete consideration of the information aggregated by all point-matching costs and enables the entire set of pairwise-image feature-mappings to be solved simultaneously and near-optimally. Our framework is capable of running in an automatic or interactive fashion, offering an effective tool for eliminating spatial misalignments introduced into sequential MSI images during the imaging process. Our experimental results obtained from a database of 28 sequences of MSI images of human eye demonstrate the superior performances of our approach to the state-of-the-art techniques. Our framework is potentially invaluable in a large variety of practical applications of MSI images. PMID:28270991

  17. Design and fabrication of Fourier spectral filter array for multispectral imaging

    NASA Astrophysics Data System (ADS)

    Ni, Chuan; Jia, Jie; Hirakawa, Keigo; Sarangan, Andrew

    2016-09-01

    Multispectral imaging has the capability to identify the state of objects based on their spectral characteristics. These are features not available with conventional color imaging based on metameric RGB (red, green and blue) colors alone. Current multispectral imaging systems use narrowband filters to capture the spectral content of a scene, which necessitates different filters to be designed and applied for each application. Previously, we demonstrated the concept of Fourier multispectral imaging using filters with sinusoidally varying transmittance [1, 2]. In this paper, we report to the design of a five channel, spatially multiplexed pixel filter array. This enables single-shot images and makes it possible to capture scenes containing moving objects.

  18. Development of multi-spectral QWIPs for extrasolar planets imaging

    NASA Astrophysics Data System (ADS)

    Nedelcu, Alexandru; Pantin, Eric

    2010-10-01

    One of the most promising approaches for direct imaging of extrasolar planets is based on the next generation of extremely large ground-based telescopes and original differential observing techniques to overcome atmospheric fluctuations problems. One possibility is the use of phase-mask coronagraphy coupled with spectral differential imaging. Multispectral Quantum Well Infrared Photodetectors (QWIPs) are a promising technological solution that could answer the stringent requirements of this challenging topic. We present here the scientific background, the technical requirements as well as the possible technical approaches that are explored in the frame of a project funded by the ANR (Agence Nationale de la Recherche). In particular, we will describe the strategy retained for the design of the QWIP active layer.

  19. Pollutant monitoring of aircraft exhaust with multispectral imaging

    NASA Astrophysics Data System (ADS)

    Berkson, Emily E.; Messinger, David W.

    2016-10-01

    Communities surrounding local airports are becoming increasingly concerned about the aircraft pollutants emitted during the landing-takeoff (LTO) cycle, and their potential for negative health effects. Chicago, Los Angeles, Boston and London have all recently been featured in the news regarding concerns over the amount of airport pollution being emitted on a daily basis, and several studies have been published on the increased risks of cancer for those living near airports. There are currently no inexpensive, portable, and unobtrusive sensors that can monitor the spatial and temporal nature of jet engine exhaust plumes. In this work we seek to design a multispectral imaging system that is capable of tracking exhaust plumes during the engine idle phase, with a specific focus on unburned hydrocarbon (UHC) emissions. UHCs are especially potent to local air quality, and their strong absorption features allow them to act as a spatial and temporal plume tracer. Using a Gaussian plume to radiometrically model jet engine exhaust, we have begun designing an inexpensive, portable, and unobtrusive imaging system to monitor the relative amount of pollutants emitted by aircraft in the idle phase. The LWIR system will use two broadband filters to detect emitted UHCs. This paper presents the spatial and temporal radiometric models of the exhaust plume from a typical jet engine used on 737s. We also select filters for plume tracking, and propose an imaging system layout for optimal detectibility. In terms of feasibility, a multispectral imaging system will be two orders of magnitude cheaper than current unobtrusive methods (PTR-MS) used to monitor jet engine emissions. Large-scale impacts of this work will include increased capabilities to monitor local airport pollution, and the potential for better-informed decision-making regarding future developments to airports.

  20. An Algorithm for Pedestrian Detection in Multispectral Image Sequences

    NASA Astrophysics Data System (ADS)

    Kniaz, V. V.; Fedorenko, V. V.

    2017-05-01

    The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

  1. 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.

  2. An innovative multimodal/multispectral image registration method for medical images based on the Expectation-Maximization algorithm.

    PubMed

    Arce-Santana, Edgar; Campos-Delgado, Daniel U; Mejia-Rodriguez, Aldo; Reducindo, Isnardo

    2015-01-01

    In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

  3. 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.

  4. Multispectral retinal image analysis: a novel non-invasive tool for retinal imaging

    PubMed Central

    Calcagni, A; Gibson, J M; Styles, I B; Claridge, E; Orihuela-Espina, F

    2011-01-01

    Purpose To develop a non-invasive method for quantification of blood and pigment distributions across the posterior pole of the fundus from multispectral images using a computer-generated reflectance model of the fundus. Methods A computer model was developed to simulate light interaction with the fundus at different wavelengths. The distribution of macular pigment (MP) and retinal haemoglobins in the fundus was obtained by comparing the model predictions with multispectral image data at each pixel. Fundus images were acquired from 16 healthy subjects from various ethnic backgrounds and parametric maps showing the distribution of MP and of retinal haemoglobins throughout the posterior pole were computed. Results The relative distributions of MP and retinal haemoglobins in the subjects were successfully derived from multispectral images acquired at wavelengths 507, 525, 552, 585, 596, and 611 nm, providing certain conditions were met and eye movement between exposures was minimal. Recovery of other fundus pigments was not feasible and further development of the imaging technique and refinement of the software are necessary to understand the full potential of multispectral retinal image analysis. Conclusion The distributions of MP and retinal haemoglobins obtained in this preliminary investigation are in good agreement with published data on normal subjects. The ongoing development of the imaging system should allow for absolute parameter values to be computed. A further study will investigate subjects with known pathologies to determine the effectiveness of the method as a screening and diagnostic tool. PMID:21904394

  5. Multispectral retinal image analysis: a novel non-invasive tool for retinal imaging.

    PubMed

    Calcagni, A; Gibson, J M; Styles, I B; Claridge, E; Orihuela-Espina, F

    2011-12-01

    To develop a non-invasive method for quantification of blood and pigment distributions across the posterior pole of the fundus from multispectral images using a computer-generated reflectance model of the fundus. A computer model was developed to simulate light interaction with the fundus at different wavelengths. The distribution of macular pigment (MP) and retinal haemoglobins in the fundus was obtained by comparing the model predictions with multispectral image data at each pixel. Fundus images were acquired from 16 healthy subjects from various ethnic backgrounds and parametric maps showing the distribution of MP and of retinal haemoglobins throughout the posterior pole were computed. The relative distributions of MP and retinal haemoglobins in the subjects were successfully derived from multispectral images acquired at wavelengths 507, 525, 552, 585, 596, and 611 nm, providing certain conditions were met and eye movement between exposures was minimal. Recovery of other fundus pigments was not feasible and further development of the imaging technique and refinement of the software are necessary to understand the full potential of multispectral retinal image analysis. The distributions of MP and retinal haemoglobins obtained in this preliminary investigation are in good agreement with published data on normal subjects. The ongoing development of the imaging system should allow for absolute parameter values to be computed. A further study will investigate subjects with known pathologies to determine the effectiveness of the method as a screening and diagnostic tool.

  6. 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.

  7. Multispectral UV Imaging for Determination of the Tablet Coating Thickness.

    PubMed

    Novikova, Anna; Carstensen, Jens M; Zeitler, J Axel; Rades, Thomas; Leopold, Claudia S

    2017-06-01

    The applicability of off-line multispectral UV imaging in combination with multivariate data analysis was investigated to determine the coating thickness and its distribution on the tablet surface during lab-scale coating. The UV imaging results were compared with the weight gain measured for each individual tablet and the corresponding coating thickness and its distribution measured by terahertz pulsed imaging (TPI). Three different tablet formulations were investigated, 2 of which contained UV-active tablet cores. Three coating formulations were applied: Aquacoat® ECD (a mainly translucent coating) and Eudragit® NE (a turbid coating containing solid particles). It was shown that UV imaging is a fast and nondestructive method to predict individual tablet weight gain as well as coating thickness. The coating thickness distribution profiles determined by UV imaging correlated to the results of the TPI measurements. UV imaging appears to hold a significant potential as a process analytical technology tool for determination of the tablet coating thickness and its distribution resulting from its high measurement speed, high molar absorptivity, and a high scattering coefficient, in addition to relatively low costs. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  8. 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%.

  9. 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

  10. Building keypoint mappings on multispectral images by a cascade of classifiers with a resurrection mechanism.

    PubMed

    Li, Yong; Jing, Jing; Jin, Hongbin; Qiao, Wei

    2015-05-21

    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.

  11. 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.

  12. 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.

  13. 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

    USDA-ARS?s Scientific Manuscript database

    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...

  14. Spatial clustering of pixels of a multispectral image

    DOEpatents

    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.

  15. Multi-spectral Line Scanner image of Northern California

    NASA Image and Video Library

    1973-06-22

    S73-34295B (June 1973) --- A vertical view of a portion of northern California reproduced from data taken from the Skylab Multispectral Scanner, experiment S192, in the Skylab space station in Earth orbit. This view is the most westerly one-third of Frame No. 001, Roll No. 518, S192, Skylab 2. Frame No. 001 extends from the Pacific coast at the Eureka area southeasterly 175 nautical miles to the Feather River drainage basin. Included in this view are Lake Shasta, Sacramento River Valley, Redding and Red Bluff. This non-photographic image is a color composite of channels 2 (visible), 7, and 12 (infrared) from the Earth Resources Experiments Package (EREP) S192 scanner. The scanner techniques assist with spectral signature identification and mapping of ground truth targets in agriculture, forestry, geology, hydrology and oceanography. Photo credit: NASA

  16. Non-invasive Imaging of Colitis using Multispectral Optoacoustic Tomography.

    PubMed

    Bhutiani, Neal; Grizzle, William E; Galandiuk, Susan; Otali, Denis; Dryden, Gerald W; Egilmez, Nejat K; McNally, Lacey R

    2016-12-01

    Currently, several non-invasive modalities, including MRI and PET, are being investigated to identify early intestinal inflammation, longitudinally monitor disease status, or detect dysplastic changes in patients with inflammatory bowel disease (IBD). Here, we assess the applicability and utility of multispectral optoacoustic tomography (MSOT) in evaluating the presence and severity of colitis. Mice with bacterial colitis demonstrated a temporally associated increase in mesenteric and colonic vascularity with an increase in mean signal intensity of oxygenated hemoglobin (p=0.004) by MSOT two days after inoculation. These findings were significantly more prominent 7 days after inoculation, with increased mean signal intensity of oxygenated hemoglobin (p=0.0002) and the development of punctate vascular lesions on the colonic surface, which corresponded to changes observed on colonoscopy as well as histology. With improvements in depth of tissue penetration, MSOT may hold potential as a sensitive, accurate, non-invasive imaging tool in evaluation of patients with IBD.

  17. Objective identification of dental abnormalities with multispectral fluorescence imaging.

    PubMed

    Singh, Surya Pratap; Fält, Pauli; Barman, Ishan; Koistinen, Arto; Dasari, Ramachandra Rao; Kullaa, Arja M

    2016-12-12

    Sensitive methods that can enable early detection of dental diseases (caries and calculus) are desirable in clinical practice. Optical spectroscopic approaches have emerged as promising alternatives owing to their wealth of molecular information and lack of sample preparation requirements. In the present study, using multispectral fluorescence imaging, we have demonstrated that dental caries and calculus can be objectively identified on extracted tooth. Spectral differences among control, carious and calculus conditions were attributed to the porphyrin pigment content, which is a byproduct of bacterial metabolism. Spectral maps generated using different porphyrin bands offer important clues to the spread of bacterial infection. Statistically significant differences utilizing fluorescence intensity ratios were observed among three groups. In contrast to laser induced fluorescence, these methods can provide information about exact spread of the infection and may aid in long term dental monitoring. Successful adoption of this approach for routine clinical usage can assist dentists in implementing timely remedial measures.

  18. 3D and multispectral imaging for subcutaneous veins detection.

    PubMed

    Paquit, Vincent C; Tobin, Kenneth W; Price, Jeffery R; Mèriaudeau, Fabrice

    2009-07-06

    The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. Experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface are presented. For illumination our system is composed of a mercury arc lamp coupled to a 10nm band-pass spectrometer. A structured lighting system is also coupled to our multispectral system in order to provide 3D information of the patient arm orientation. Images of each patient arm are captured under every possible combinations of illuminants and the optimal combination of wavelengths for a given subject to maximize vein contrast using linear discriminant analysis is determined.

  19. 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.

  20. 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

  1. Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

    NASA Astrophysics Data System (ADS)

    Bigdeli, Behnaz; Pahlavani, Parham

    2017-01-01

    Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

  2. Mineral Classification of the Martian Surface Using THEMIS Multi-Spectral Infrared Imagery

    NASA Astrophysics Data System (ADS)

    Osterloo, M. M.; Brumby, S. P.; Funsten, H. O.; Feldman, W. C.

    2004-12-01

    Recent advancements in multi-spectral imaging and image analysis techniques have greatly enhanced our ability to do planetary research. Much has been discovered about Mars through recent missions such as Mars Global Surveyor, 2001 Mars Odyssey, and the Mars Exploration Rovers. The Thermal Emission Spectrometer on board the Mars Global Surveyor has allowed the mapping of surface mineralogies on Mars at several kilometers scale through hyperspectral imaging [1]. Here, we use the high resolution multi-spectral imagery of THEMIS (THermal Emission Imaging System) on board the 2001 Mars Odyssey to identify different mineral classes at spatial scales of hundreds of meters. THEMIS contains two independent multi-spectral imaging systems: a 10-band thermal infrared imager (IR) with a resolution of 100m/pixel, and a 5-band visible imager with a resolution of 10m/pixel. Here we will use the IR data. The 9 IR bands are centered from 6.8 microns to 14 .9 microns [2]. Using Arizona State University's online spectral library[3], we have been investigating the extent to which we can differentiate between different mineral classes. By identifying certain mineral classes we can better understand the geologic processes which created them and detect areas of interest for further study. Linear mixing of minerals and dust is investigated to estimate ratios of minerals and their resulting spectra. We then compare these spectra to observations of several regions on Mars. We compare these results with TES data and previous mineralogical maps. [1] Christensen et al, (2001) JGR 106, E10; [2] Christensen et al, (2002) Space Science Reviews 110, 1; [3] Christensen et al, (2000) JGR 105, E4

  3. Automated endmember extraction for subpixel classification of multispectral and hyperspectral data

    NASA Astrophysics Data System (ADS)

    Shrivastava, Deepali; Kumar, Vinay; Sharma, Richa U.

    2016-04-01

    Most of the multispectral sensors acquire data in several broad wavelength bands and are capable of extracting different Land Cover features while hyperspectral sensors contain ample spectral data in narrow bandwidth (10- 20nm). The spectrally rich data enable the extraction of useful quantitative information from earth surface features. Endmembers are the pure spectral components extracted from the remote sensing datasets. Most approaches for Endmember extraction (EME) are manual and have been designed from a spectroscopic viewpoint, thus neglecting the spatial arrangement of the pixels. Therefore, EME techniques which can consider both spectral and spatial aspects are required to find more accurate Endmembers for Subpixel classification. Multispectral (EO-1 ALI and Landsat 8 OLI) and Hyperspectral (EO-1 Hyperion) datasets of Udaipur region, Rajasthan is used in this study. All the above mentioned datasets are preprocessed and converted to surface reflectance using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH). Further Automated Endmember extraction and Subpixel classification is carried out using Multiple Endmember Spectral Mixture Analysis (MESMA). Endmembers are selected from spectral libraries to be given as input to MESMA. To optimize these spectral libraries three techniques are deployed i.e. Count based Endmember selection (CoB), Endmember Average RMSE (EAR) and Minimum Average Spectral Angle (MASA) for endmember selection. Further identified endmembers are used for classifying multispectral and hyperspectral data using MESMA and SAM. It was observed from the obtained classified results that diverse features, spread over a pixel, which are spectrally same are well classified by MESMA whereas SAM was unable to do so.

  4. Multispectral image segmentation using parallel mean shift algorithm and CUDA technology

    NASA Astrophysics Data System (ADS)

    Zghidi, Hafedh; Walczak, Maksym; Świtoński, Adam

    2016-06-01

    We present a parallel mean shift algorithm running on CUDA and its possible application in segmentation of multispectral images. The aim of this paper is to present a method of analyzing highly noised multispectral images of various objects, so that important features are enhanced and easier to identify. The algorithm finds applications in analysis of multispectral images of eyes so that certain features visible only in specific wavelengths are made clearly visible despite high level of noise, for which processing time is very long.

  5. MathWeb: a concurrent image analysis tool suite for multispectral data fusion

    NASA Astrophysics Data System (ADS)

    Achalakul, Tiranee; Haaland, Peter D.; Taylor, Stephen

    1999-03-01

    This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect cancer cell structures. The approach combines a multi-spectral microscope with an image analysis tool suite, MathWeb. The tool suite incorporates a concurrent Principal Component Transform (PCT) that is used to fuse the multi-spectral data. This paper describes the general approach and the concurrent PCT algorithm. The algorithm is evaluated from both the perspective of image quality and performance scalability.

  6. High-contrast subcutaneous vein detection and localization using multispectral imaging

    PubMed Central

    Behrooz, Ali; Morris, Michael; Adibi, Ali

    2013-01-01

    Abstract. Multispectral imaging has shown promise in subcutaneous vein detection and localization in human subjects. While many limitations of single-wavelength methods are addressed in multispectral vein detection methods, their performance is still limited by artifacts arising from background skin reflectance and optimality of postprocessing algorithms. We propose a background removal technique that enhances the contrast and performance of multispectral vein detection. We use images acquired at visible wavelengths as reference for removing skin reflectance background from subcutaneous structures in near-infrared images. Results are validated by experiments on human subjects. PMID:23649005

  7. High-contrast subcutaneous vein detection and localization using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Wang, Fengtao; Behrooz, Ali; Morris, Michael; Adibi, Ali

    2013-05-01

    Multispectral imaging has shown promise in subcutaneous vein detection and localization in human subjects. While many limitations of single-wavelength methods are addressed in multispectral vein detection methods, their performance is still limited by artifacts arising from background skin reflectance and optimality of postprocessing algorithms. We propose a background removal technique that enhances the contrast and performance of multispectral vein detection. We use images acquired at visible wavelengths as reference for removing skin reflectance background from subcutaneous structures in near-infrared images. Results are validated by experiments on human subjects.

  8. Comprehensive evaluation for fused images of multispectral and panchromatic images based on entropy weight method

    NASA Astrophysics Data System (ADS)

    Xia, Xiaojie; Yuan, Yan; Su, Lijuan; Hu, Liang

    2016-09-01

    An evaluation model of image fusion based on entropy weight method is put forward to resolve evaluation issue for fused results of multispectral and panchromatic images, such as the lack of overall importance in single factor metric evaluation and the discrepancy among different categories of characteristic evaluation. In this way, several single factor metrics in different aspects of image are selected to form a metric set, then the entropy weights for each single factor index are calculated based on entropy weight method, thus a new comprehensive evaluation index is obtained to evaluate each fused image and images with higher spectral resolution and spatial resolution can be acquired. Experimental analysis shows that the proposed method is of versatility, objectivity and rationality and performs well on the evaluation of fused results of multispectral and panchromatic images.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents

    PubMed Central

    Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

    2017-01-01

    The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions. PMID:28098797

  18. Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents.

    PubMed

    Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

    2017-01-13

    The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

  19. Image classification using eigenpaxels

    NASA Astrophysics Data System (ADS)

    McGuire, Peter Frederick

    The intelligent control of robotic is a major limiting factor in the utilization of current robotic technology. Although the technology to accurately position robotic manipulators is well developed, practical applications are often limited by the controller's ability to interact with a complex environment. Central to this plight is the integration of sensory signals, such as vision, into the control structure. Recently, a number of promising approaches to visual information processing have been developed using artificial neural networks (ANNs). These approaches, however, are often tailored to particular applications and are therefore disparate and limited in scope. In contrast, biological neural networks perform a wide range of visual tasks yet this behavior arises from a single integrated neural structure. The work presented in this thesis details a biologically inspired image processing algorithm and its application to an image classification problem. Based on the organization of cells in the primary visual cortex of primates, this algorithm utilizes key neural mechanisms to produce efficient representations of images. Dubbed the "eigenpaxel" algorithm, excellent results are obtained despite the relative simplicity of the method. In addition, the relationship between the algorithm and biological vision may help to shed light on the processing occurring within the brain and the basis of the organization found therein.

  20. 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.

  1. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

    PubMed Central

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L.; Hwang, Jae Youn

    2016-01-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. PMID:28018743

  2. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

    PubMed

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L; Hwang, Jae Youn

    2016-12-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.

  3. 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.

  4. 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.

  5. 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.

  6. A method for comparison of growth media in objective identification of Penicillium based on multi-spectral imaging.

    PubMed

    Clemmensen, Line H; Hansen, Michael E; Frisvad, Jens C; Ersbøll, Bjarne K

    2007-05-01

    We consider the problems of using excessive growth media for identification and performing objective identification of fungi at the species level. We propose a method for choosing the subset of growth media, which provides the best discrimination between several fungal species. Furthermore, we propose the use of multi-spectral imaging as a means of objective identification. Three species of the fungal genus Penicillium are subject to classification. To obtain an objective classification we use multi-spectral images. Previously, RGB images have proven useful for the purpose. We use multi-spectral bands as they provide additional information about the chemistry of the fungal colonies. In this study three media [Czapek yeast extract agar (CYA), oatmeal agar (OAT), and yeast extract sucrose agar (YES)] have been compared on their ability to discriminate between the three species. We propose a statistical method to test which medium or combination of media gives the best discrimination. Statistical tests indicate that YES combined with CYA is the best choice of media in this case. However, for the objective identification one medium is sufficient to discriminate between the species. Statistical tests show that there are significant differences between the species on all individual media, and that these differences are largest on YES. The objective identification has been performed solely by means of digital image analysis. The features obtained from the image analysis merely correspond to macro-morphological features. The species have been classified using only 3-4 of the spectral bands with a 100% correct classification rate using both leave-one-out cross-validation and test set validation.

  7. Multispectral Cerenkov luminescence tomography for small animal optical imaging.

    PubMed

    Spinelli, Antonello E; Kuo, Chaincy; Rice, Brad W; Calandrino, Riccardo; Marzola, Pasquina; Sbarbati, Andrea; Boschi, Federico

    2011-06-20

    Quite recently Cerenkov luminescence imaging (CLI) has been introduced as a novel pre-clinical imaging for the in vivo imaging of small animals such as mice. The CLI method is based on the detection of Cerenkov radiation (CR) generated by beta particles as they travel into the animal tissues with an energy such that Cerenkov emission condition is satisfied. This paper describes an image reconstruction method called multi spectral diffuse Cerenkov luminescence tomography (msCLT) in order to obtain 3D images from the detection of CR. The multispectral approach is based on a set of 2D planar images acquired using a number of narrow bandpass filters, and the distinctive information content at each wavelength is used in the 3D image reconstruction process. The proposed msCLT method was tested both in vitro and in vivo using 32P-ATP and all the images were acquired by using the IVIS 200 small animal optical imager (Caliper Life Sciences, Alameda USA). Source depth estimation and spatial resolution measurements were performed using a small capillary source placed between several slices of chicken breast. The theoretical Cerenkov emission spectrum and optical properties of chicken breast were used in the modelling of photon propagation. In vivo imaging was performed by injecting control nude mice with 10 MBq of 32P-ATP and the 3D tracer bio-distribution was reconstructed. Whole body MRI was acquired to provide an anatomical localization of the Cerenkov emission. The spatial resolution obtained from the msCLT reconstructed images of the capillary source showed that the FWHM is about 1.5 mm for a 6 mm depth. Co-registered MRI images showed that the Cerenkov emission regions matches fairly well with anatomical regions, such as the brain, heart and abdomen. Ex vivo imaging of the different organs such as intestine, brain, heart and ribs further confirms these findings. We conclude that in vivo 3D bio-distribution of a pure beta-minus emitting radiopharmaceutical such as 32P

  8. Sensitive segmentation of low-contrast multispectral images based on multiparameter space-resonance imaging method

    NASA Astrophysics Data System (ADS)

    Akhmetshin, Alexander M.; Akhmetshin, Lyudmila G.

    2001-10-01

    A new method of low contrast multispectral, hyperspectral and multiparameter images segmentation is outlined. The one has significant advantage in sensitivity and space resolving power of segmentation in comparison with known methods such as principal component transformation and fuzzy C-means clustering segmentation ones. New method is based on using of two important stages: 1) application virtual long-wave holographic transformation to each separate image of analyzed multispectral sequence (it is needed for increasing sensitivity of further analysis); 2) to each pixel of analyzed multispectral image is compare a virtual nonrecursive digital filter with complex coefficients. The one is characterized by its amplitude-frequency (AFC) and phase-frequency (PFC) characteristics. Information features used for visualization and segmentation are frequencies corresponded to maximum (resonance point) or minimum (antiresonance point) of AFC and group delay function calculated on base PFC. Information possibilities of new method are demonstrated on examples of multispectral remote sensing, various physical nature geophysical fields fusion and multiparameter MRI brain tumor hidden area influence detection.

  9. Novel image fusion scheme based on maximum ratio combining for robust multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Omri, Faten; Foufou, Sebti

    2015-04-01

    Recently, the research in multispectral face recognition has focused on developing efficient frameworks for improving face recognition performance at close-up distances. However, few studies have investigated the multispectral face images captured at long distance. In fact, great challenges still exist in recognizing human face in images captured at long distance as the image quality might be affected and some important features masked. Therefore, multispectral face recognition tools and algorithms should evolve from close-up distances to long distances. To address these issues, we present in this paper a novel image fusion scheme based on Maximum Ratio Combining algorithm and improve multispectral face recognition at long distance. The proposed method is compared with similar super-resolution method based on the Maximum likelihood algorithm. Simulation results show the efficiency of the proposed approach in term of average variance of detection error.

  10. Feasibility study and quality assessment of unmanned aircraft system-derived multispectral images

    NASA Astrophysics Data System (ADS)

    Chang, Kuo-Jen

    2017-04-01

    The purpose of study is to explore the precision and the applicability of UAS-derived multispectral images. In this study, the Micro-MCA6 multispectral camera was mounted on quadcopter. The Micro-MCA6 shoot images synchronized of each single band. By means of geotagged images and control points, the orthomosaic images of each single band generated firstly by 14cm resolution. The multispectral image was merged complete with 6 bands. In order to improve the spatial resolution, the 6 band image fused with 9cm resolution image taken from RGB camera. Quality evaluation of the image is verified of the each single band by using control points and check points. The standard deviations of errors are within 1 to 2 pixel resolution of each band. The quality of the multispectral image is compared with 3 cm resolution orthomosaic RGB image gathered from UAV in the same mission, as well. The standard deviations of errors are within 2 to 3 pixel resolution. The result shows that the errors resulting from the blurry and the band dislocation of the objects edge identification. To the end, the normalized difference vegetation index (NDVI) extracted from the image to explore the condition of vegetation and the nature of the environment. This study demonstrates the feasibility and the capability of the high resolution multispectral images.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Multispectral image fusion for vehicle identification and threat analysis

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2016-05-01

    Unauthorized vehicles become an increasing threat to US facilities and locations especially overseas. Vehicle detection is a well-studied area. However, vehicle identification and intension analysis have not been sufficiently investigated. We propose to use multispectral (visible, thermal) images (1) to match the vehicle types with the registered (or authorized) vehicle types; (2) to analyze the vehicle moving patterns, (3) and study methods to utilize open information such as GPS and traffic information. When a vehicle is either permitted to access to the facility, or subjected to further manual inspection (scrutiny), the additional information (e.g., text) can be compared against the imagery features. We use information fusion (at image, feature, and score level) and neural network to increase vehicle matching accuracy. For the vehicle moving patterns, we will classify them as "normal" and "abnormal" by using driving speed, acceleration, stop, zig-zag, etc. The methods would support directions in physical and human-based sensor fusion, patterns of life (POL) analysis, and contextual-enhanced information fusion.

  16. 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.

  17. Laser multi-spectral polarimetric diffuse-scatter imaging

    NASA Astrophysics Data System (ADS)

    Wang, Yang

    Laser multi-spectral polarimetric diffuse scatter (LAMPODS) imaging is an approach that maps an object intrinsic optical scattering properties rather than the scattered light intensity like in conventional imaging. The technique involves comprehensive measurements of the object scattering response function that is to be parameterized with respect to wavelength, polarization, and angular scattering distribution. The LAMPODS images are mappings of the derived parameters, which are more fundamental than conventional images. The LAMPODS imaging system was built based on a system architecture design configured similarly to an optical wireless network that allows multiple communication connections simultaneously among any number of transmitters and receivers. The imaging system was implemented into several sets of experimental apparatuses that can detect Stokes vectors of backward and forward scattered light with laser sources at seven near infrared (NIR) wavelengths and a continuous mid-infrared (mid-IR) spectral range for both macroscopic and microscopic scan imaging applications. The system components, such as transmitters, receivers, image scan unit, and the data acquisition module, were built and/or tested to match the system-design requirements, which involved many optical, opto-mechanical, electronic, and computer programming/interfacing techniques and skills. The experiments performed include the study on the LAMPODS capability with isolated aspects of scattering response, and the test of LAMPODS on uncontrolled subjects. With special-made targets, the results indicate that the LAMPODS system can distinguish consistently the four produced random surface roughnesses, regardless of the subjects? Spectroscopic signature, and can separate the spectroscopic features independently. Various natural and man-made targets were tested to challenge the LAMPODS system capability and found many interesting features regarding spectral response, polarimetric response, and

  18. 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

  19. Real-time aerial multispectral imaging solutions using dichroic filter arrays

    NASA Astrophysics Data System (ADS)

    Chandler, Eric V.; Fish, David E.

    2014-06-01

    The next generation of multispectral sensors and cameras needs to deliver significant improvements in size, weight, portability, and spectral band customization to support widespread commercial deployment for a variety of purposebuilt aerial, unmanned, and scientific applications. The benefits of multispectral imaging are well established for applications including machine vision, biomedical, authentication, and remote sensing environments - but many aerial and OEM solutions require more compact, robust, and cost-effective production cameras to realize these benefits. A novel implementation uses micropatterning of dichroic filters into Bayer and custom mosaics, enabling true real-time multispectral imaging with simultaneous multi-band image acquisition. Consistent with color camera image processing, individual spectral channels are de-mosaiced with each channel providing an image of the field of view. We demonstrate recent results of 4-9 band dichroic filter arrays in multispectral cameras using a variety of sensors including linear, area, silicon, and InGaAs. Specific implementations range from hybrid RGB + NIR sensors to custom sensors with applicationspecific VIS, NIR, and SWIR spectral bands. Benefits and tradeoffs of multispectral sensors using dichroic filter arrays are compared with alternative approaches - including their passivity, spectral range, customization options, and development path. Finally, we report on the wafer-level fabrication of dichroic filter arrays on imaging sensors for scalable production of multispectral sensors and cameras.

  20. Real-time compact multispectral imaging solutions using dichroic filter arrays

    NASA Astrophysics Data System (ADS)

    Chandler, Eric V.; Fish, David E.

    2014-03-01

    The next generation of multispectral sensors and cameras will need to deliver significant improvements in size, weight, portability, and spectral band customization to support widespread commercial deployment. The benefits of multispectral imaging are well established for applications including machine vision, biomedical, authentication, and aerial remote sensing environments - but many OEM solutions require more compact, robust, and cost-effective production cameras to realize these benefits. A novel implementation uses micro-patterning of dichroic filters into Bayer and custom mosaics, enabling true real-time multispectral imaging with simultaneous multi-band image acquisition. Consistent with color camera image processing, individual spectral channels are de-mosaiced with each channel providing an image of the field of view. We demonstrate recent results of 4-9 band dichroic filter arrays in multispectral cameras using a variety of sensors including linear, area, silicon, and InGaAs. Specific implementations range from hybrid RGB + NIR sensors to custom sensors with application-specific VIS, NIR, and SWIR spectral bands. Benefits and tradeoffs of multispectral sensors using dichroic filter arrays are compared with alternative approaches - including their passivity, spectral range, customization options, and development path. Finally, we report on the wafer-level fabrication of dichroic filter arrays on imaging sensors for scalable production of multispectral sensors and cameras.

  1. The effect of multispectral image fusion enhancement on human efficiency.

    PubMed

    Bittner, Jennifer L; Schill, M Trent; Mohd-Zaid, Fairul; Blaha, Leslie M

    2017-01-01

    The visual system can be highly influenced by changes to visual presentation. Thus, numerous techniques have been developed to augment imagery in an attempt to improve human perception. The current paper examines the potential impact of one such enhancement, multispectral image fusion, where imagery captured in varying spectral bands (e.g., visible, thermal, night vision) is algorithmically combined to produce an output to strengthen visual perception. We employ ideal observer analysis over a series of experimental conditions to (1) establish a framework for testing the impact of image fusion over the varying aspects surrounding its implementation (e.g., stimulus content, task) and (2) examine the effectiveness of fusion on human information processing efficiency in a basic application. We used a set of rotated Landolt C images captured with a number of individual sensor cameras and combined across seven traditional fusion algorithms (e.g., Laplacian pyramid, principal component analysis, averaging) in a 1-of-8 orientation task. We found that, contrary to the idea of fused imagery always producing a greater impact on perception, single-band imagery can be just as influential. Additionally, efficiency data were shown to fluctuate based on sensor combination instead of fusion algorithm, suggesting the need for examining multiple factors to determine the success of image fusion. Our use of ideal observer analysis, a popular technique from the vision sciences, provides not only a standard for testing fusion in direct relation to the visual system but also allows for comparable examination of fusion across its associated problem space of application.

  2. 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.

  3. [A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

    PubMed

    Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

    2011-10-01

    Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

  4. 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

  5. Number of signatures necessary for accurate classification. [for multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Richardson, W.; Pentland, A.; Crane, R.; Horwitz, H.

    1976-01-01

    This paper presents a procedure for determining the number of signatures to use in classifying multispectral scanner data. A large initial set of signatures is obtained by clustering the training points within each category (such as 'wheat' or 'other') to be recognized. These clusters are then combined into broader signatures by a program that considers each pair of signatures within a category, combines the best pair in the light of certain criteria, saves the combined signature and repeats the procedure until there is one signature for each category. The result is a collection of sets of signatures, one set for each number between the number of initial clusters and the number of categories. With the aid of statistics such as an estimate of the probability of misclassification between categories, the user can choose the smallest set satisfying his requirements for classification accuracy.

  6. Multi-spectral image enhancement algorithm based on keeping original gray level

    NASA Astrophysics Data System (ADS)

    Wang, Tian; Xu, Linli; Yang, Weiping

    2016-11-01

    Characteristics of multi-spectral imaging system and the image enhancement algorithm are introduced.Because histogram equalization and some other image enhancement will change the original gray level,a new image enhancement algorithm is proposed to maintain the gray level.For this paper, we have chosen 6 narrow-bands multi-spectral images to compare,the experimental results show that the proposed method is better than those histogram equalization and other algorithm to multi-spectral images.It also insures that histogram information contained in original features is preserved and guarantees to make use of data class information.What's more,on the combination of subjective and objective sharpness evaluation,details of the images are enhanced and noise is weaken.

  7. 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.

  8. 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.

  9. A new clustering algorithm applicable to multispectral and polarimetric SAR images

    NASA Technical Reports Server (NTRS)

    Wong, Yiu-Fai; Posner, Edward C.

    1993-01-01

    We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

  10. A new clustering algorithm applicable to multispectral and polarimetric SAR images

    NASA Technical Reports Server (NTRS)

    Wong, Yiu-Fai; Posner, Edward C.

    1993-01-01

    We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

  11. Deep imaging of absorption and scattering features by multispectral multiple scattering low coherence interferometry

    PubMed Central

    Zhao, Yang; Maher, Jason R.; Ibrahim, Mohamed M.; Chien, Jennifer S.; Levinson, Howard; Wax, Adam

    2016-01-01

    We have developed frequency domain multispectral multiple scattering low coherence interferometry (ms2/LCI) for deep imaging of absorption and scattering contrast. Using tissue-mimicking phantoms that match the full scattering phase function of human dermal tissue, we demonstrate that ms2/LCI can provide a signal/noise ratio (SNR) improvement of 15.4 dB over conventional OCT at an imaging depth of 1 mm. The enhanced SNR and penetration depth provided by ms2/LCI could be leveraged for a variety of clinical applications including the assessment of burn injuries where current clinical classification of severity only provides limited accuracy. The utility of the approach was demonstrated by imaging a tissue phantom simulating a partial-thickness burn revealing good spectroscopic contrast between healthy and injured tissue regions deep below the sample surface. Finally, healthy rat skin was imaged in vivo with both a commercial OCT instrument and our custom ms2/LCI system. The results demonstrate that ms2/LCI is capable of obtaining spectroscopic information far beyond the penetration depth provided by conventional OCT. PMID:27867703

  12. 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.

  13. a High-Efficiency Fusion Method of Multi-Spectral Image and Panchromatic Image

    NASA Astrophysics Data System (ADS)

    Xue, X.; Wang, J. P.; Wang, H.; Xiang, F.

    2013-07-01

    With the development of modern remote sensing technology, a variety of earth observation satellites could continue to tremendously provide image data of different spatial resolution, time resolution, spectral resolution remote sensing, and the remote sensing data obtained is increasing with great capacity, which forms multi-source image pyramid in the same area. To play the advantages of a variety of remote sensing data, the application of remote sensing image fusion is a very important choice. When remote sensing data is large, fusion is large in computing capacity and time-consuming, so it is difficult to carry out rapid, real-time fusion. However, in some remote sensing applications, such as disaster prevention and relief quick, etc., timely fusion is required. Based on image fusion method of principal component analysis (PCA) and the advantage of parallel computing, a high-efficiency fusion method of multi-spectral image and panchromatic image is proposed. Beijing-1 Micro-satellite is a high-performance small satellite for earth observation,With Beijing-1 Micro-satellite remote sensing images as the experimental data, it is proved that good fusion results of multi-spectral image and panchromatic image can be obtained with the proposed method, and the fusion speed is also fast. At the same time, some measures of improving the efficiency of parallel image fusion are also discussed.

  14. 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

  15. 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.

  16. 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.

  17. Dynamic multispectral imaging system with spectral zooming capability and its applications

    NASA Astrophysics Data System (ADS)

    Chen, Bing

    The main focus of this dissertation is to develop a multispectral imaging system with spectral zooming capability and also successfully demonstrate its promising medical applications through combining this technique with microscope system. The realization of the multispectral imaging method in this dissertation is based on the 4-f spatial filtering principle. When a collimated light is dispersed by the grating, there exists a clear linear distribution spectral line or spectrum at the Fourier plane of the Fourier transform lens group base on the Abbe imaging theory and optics Fourier Transform principle. The optical images, not the collimated light, are applied into this setup and the spectrum distribution still keeps linear relationship with the spatial positions at Fourier plane, even through there exists additional spectral crosstalk or overlap. The spatial filter or dynamic electrical filters used at the Fourier plane will facilitate randomly access the desired spectral waveband and agilely adjust the passband width. It offers the multispectral imaging functionality with spectral zooming capability. The system is flexible and efficiency. A dual-channel spectral imaging system based on the multispectral imaging method and acousto-optical tunable filter (AOTF) is proposed in the dissertation. The multispectral imaging method and the AOTF will form spate imaging channels and the two spectral channels work together to enhance the system efficiency. The AOTF retro reflection design is explored in the dissertation and experimental results demonstrate this design could effectively improve the spectral resolution of the passband. Moreover, a field lens is introduced into the multispectral imaging system to enhance the field of view of the system detection range. The application of field lens also improves the system spectral resolution, image quality and minimizes the system size. This spectral imaging system can be used for many applications. The compact prototype

  18. 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

  19. 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.

  20. 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).

  1. Remote multispectral imaging with PRISMS and XRF analysis of Tang tomb paintings

    NASA Astrophysics Data System (ADS)

    Lange, Rebecca; Zhang, Qunxi; Liang, Haida

    2011-06-01

    PRISMS (Portable Remote Imaging System for Multispectral Scanning) is a multispectral/hyperspectral imaging system designed for flexible in situ imaging of wall paintings at high resolution (tens of microns) over a large range of distances (less than a meter to over ten meters). This paper demonstrates a trial run of the VIS/NIR (400-880nm) component of the instrument for non-invasive imaging of wall paintings in situ. Wall painting panels from excavated Tang dynasty (618- 907AD) tombs near Xi'an were examined by PRISMS. Pigment identifications were carried out using the spectral reflectance obtained from multispectral imaging coupled with non-invasive elemental analysis using a portable XRF.

  2. Application of multispectral imaging detects areas with neuronal myelin loss, without tissue labelling.

    PubMed

    Vazgiouraki, Eleftheria; Papadakis, Vassilis M; Efstathopoulos, Paschalis; Lazaridis, Iakovos; Charalampopoulos, Ioannis; Fotakis, Costas; Gravanis, Achille

    2016-04-01

    The application of multispectral imaging to discriminate myelinated and demyelinated areas of neural tissue is herein presented. The method is applied through a custom-made, multispectral imaging monochromator, coupled to a commercially available microscope. In the present work, a series of spinal cord sections were analysed derived from mice with experimental autoimmune encephalomyelitis (EAE), an experimental model widely used to study multiple sclerosis (MS). The multispectral microscope allows imaging of local areas with loss of myelin without the need of tissue labelling. Imaging with the aforementioned method and system is compared in a parallel way with conventional methods (wide-field and confocal fluorescence microscopies). The diagnostic sensitivity of our method is 90.4% relative to the 'gold standard' method of immunofluorescence microscopy. The presented method offers a new platform for the possible future development of an in vivo, real-time, non-invasive, rapid imaging diagnostic tool of spinal cord myelin loss-derived pathologies.

  3. Quantitative imaging of tumor vasculature using multispectral optoacoustic tomography (MSOT)

    NASA Astrophysics Data System (ADS)

    Tomaszewski, Michal R.; Quiros-Gonzalez, Isabel; Joseph, James; Bohndiek, Sarah E.

    2017-03-01

    The ability to evaluate tumor oxygenation in the clinic could indicate prognosis and enable treatment monitoring, since oxygen deficient cancer cells are often more resistant to chemotherapy and radiotherapy. MultiSpectral Optoacoustic Tomography (MSOT) is a hybrid technique combining the high contrast of optical imaging with spatial resolution and penetration depth similar to ultrasound. We hypothesized that MSOT could reveal both tumor vascular density and function based on modulation of blood oxygenation. We performed MSOT on nude mice (n=8) bearing subcutaneous xenograft PC3 tumors using an inVision 256 (iThera Medical). The mice were maintained under inhalation anesthesia during imaging and respired oxygen content was modified from 21% to 100% and back. After imaging, Hoechst 33348 was injected to indicate vascular perfusion and permeability. Tumors were then extracted for histopathological analysis and fluorescence microscopy. The acquired data was analyzed to extract a bulk measurement of blood oxygenation (SO2MSOT) from the whole tumor using different approaches. The tumors were also automatically segmented into 5 regions to investigate the effect of depth on SO2MSOT. Baseline SO2MSOT values at 21% and 100% oxygen breathing showed no relationship with ex vivo measures of vascular density or function, while the change in SO2MSOT showed a strong negative correlation to Hoechst intensity (r=- 0.92, p=0.0016). Tumor voxels responding to oxygen challenge were spatially heterogeneous. We observed a significant drop in SO2 MSOT value with tumor depth following a switch of respiratory gas from air to oxygen (0.323+/-0.017 vs. 0.11+/-0.05, p=0.009 between 0 and 1.5mm depth), but no such effect for air breathing (0.265+/-0.013 vs. 0.19+/-0.04, p=0.14 between 0 and 1.5mm depth). Our results indicate that in subcutaneous prostate tumors, baseline SO2MSOT levels do not correlate to tumor vascular density or function while the magnitude of the response to oxygen

  4. Multispectral code excited linear prediction coding and its application in magnetic resonance images.

    PubMed

    Hu, J H; Wang, Y; Cahill, P T

    1997-01-01

    This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

  5. Detection of melanoma metastases in resected human lymph nodes by noninvasive multispectral photoacoustic imaging.

    PubMed

    Langhout, Gerrit Cornelis; Grootendorst, Diederik Johannes; Nieweg, Omgo Edo; Wouters, Michel Wilhelmus Jacobus Maria; van der Hage, Jos Alexander; Jose, Jithin; van Boven, Hester; Steenbergen, Wiendelt; Manohar, Srirang; Ruers, Theodoor Jacques Marie

    2014-01-01

    Objective. Sentinel node biopsy in patients with cutaneous melanoma improves staging, provides prognostic information, and leads to an increased survival in node-positive patients. However, frozen section analysis of the sentinel node is not reliable and definitive histopathology evaluation requires days, preventing intraoperative decision-making and immediate therapy. Photoacoustic imaging can evaluate intact lymph nodes, but specificity can be hampered by other absorbers such as hemoglobin. Near infrared multispectral photoacoustic imaging is a new approach that has the potential to selectively detect melanin. The purpose of the present study is to examine the potential of multispectral photoacoustic imaging to identify melanoma metastasis in human lymph nodes. Methods. Three metastatic and nine benign lymph nodes from eight melanoma patients were scanned ex vivo using a Vevo LAZR(©) multispectral photoacoustic imager and were spectrally analyzed per pixel. The results were compared to histopathology as gold standard. Results. The nodal volume could be scanned within 20 minutes. An unmixing procedure was proposed to identify melanoma metastases with multispectral photoacoustic imaging. Ultrasound overlay enabled anatomical correlation. The penetration depth of the photoacoustic signal was up to 2 cm. Conclusion. Multispectral three-dimensional photoacoustic imaging allowed for selective identification of melanoma metastases in human lymph nodes.

  6. Multispectral image alignment using a three channel endoscope in vivo during minimally invasive surgery.

    PubMed

    Clancy, Neil T; Stoyanov, Danail; James, David R C; Di Marco, Aimee; Sauvage, Vincent; Clark, James; Yang, Guang-Zhong; Elson, Daniel S

    2012-10-01

    Sequential multispectral imaging is an acquisition technique that involves collecting images of a target at different wavelengths, to compile a spectrum for each pixel. In surgical applications it suffers from low illumination levels and motion artefacts. A three-channel rigid endoscope system has been developed that allows simultaneous recording of stereoscopic and multispectral images. Salient features on the tissue surface may be tracked during the acquisition in the stereo cameras and, using multiple camera triangulation techniques, this information used to align the multispectral images automatically even though the tissue or camera is moving. This paper describes a detailed validation of the set-up in a controlled experiment before presenting the first in vivo use of the device in a porcine minimally invasive surgical procedure. Multispectral images of the large bowel were acquired and used to extract the relative concentration of haemoglobin in the tissue despite motion due to breathing during the acquisition. Using the stereoscopic information it was also possible to overlay the multispectral information on the reconstructed 3D surface. This experiment demonstrates the ability of this system for measuring blood perfusion changes in the tissue during surgery and its potential use as a platform for other sequential imaging modalities.

  7. Map Classification In Image Data

    DTIC Science & Technology

    2015-09-25

    NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MAP CLASSIFICATION IN IMAGE DATA by Frank Fiebiger September 2015 Thesis Advisor: Mathias N...Leave Blank) 2. REPORT DATE 09-25-2015 3. REPORT TYPE AND DATES COVERED Master’s Thesis 10-01-2014 to 09-25-2015 4. TITLE AND SUBTITLE MAP ...the proliferation of image data. This thesis addresses the specific problem of distinguishing two-dimensional map images from other image content by

  8. 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.

  9. [Nitrogen stress measurement of canola based on multi-spectral charged coupled device imaging sensor].

    PubMed

    Feng, Lei; Fang, Hui; Zhou, Wei-Jun; Huang, Min; He, Yong

    2006-09-01

    Site-specific variable nitrogen application is one of the major precision crop production management operations. Obtaining sufficient crop nitrogen stress information is essential for achieving effective site-specific nitrogen applications. The present paper describes the development of a multi-spectral nitrogen deficiency sensor, which uses three channels (green, red, near-infrared) of crop images to determine the nitrogen level of canola. This sensor assesses the nitrogen stress by means of estimated SPAD value of the canola based on canola canopy reflectance sensed using three channels (green, red, near-infrared) of the multi-spectral camera. The core of this investigation is the calibration methods between the multi-spectral references and the nitrogen levels in crops measured using a SPAD 502 chlorophyll meter. Based on the results obtained from this study, it can be concluded that a multi-spectral CCD camera can provide sufficient information to perform reasonable SPAD values estimation during field operations.

  10. Undercomplete learned dictionaries for land cover classification in multispectral imagery of Arctic landscapes using CoSA: clustering of sparse approximations

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Gangodagamage, Chandana

    2013-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. 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 our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.

  11. A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

    PubMed Central

    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. PMID:24453808

  12. Quantifying autophagy: Measuring LC3 puncta and autolysosome formation in cells using multispectral imaging flow cytometry.

    PubMed

    Pugsley, Haley R

    2017-01-01

    The use of multispectral imaging flow cytometry has been gaining popularity due to its quantitative power, high throughput capabilities, multiplexing potential and its ability to acquire images of every cell. Autophagy is a process in which dysfunctional organelles and cellular components that accumulate during growth and differentiation are degraded via the lysosome and recycled. During autophagy, cytoplasmic LC3 is processed and recruited to the autophagosomal membranes; the autophagosome then fuses with the lysosome to form the autolysosome. Therefore, cells undergoing autophagy can be identified by visualizing fluorescently labeled LC3 puncta and/or the co-localization of fluorescently labeled LC3 and lysosomal markers. Multispectral imaging flow cytometry is able to collect imagery of large numbers of cells and assess autophagy in an objective, quantitative, and statistically robust manner. This review will examine the four predominant methods that have been used to measure autophagy via multispectral imaging flow cytometry.

  13. Multispectral imaging of pigmented and vascular cutaneous malformations: the influence of laser treatment

    NASA Astrophysics Data System (ADS)

    Kuzmina, Ilona; Diebele, Ilze; Asare, Lasma; Kempele, Anna; Abelite, Anita; Jakovels, Dainis; Spigulis, Janis

    2010-11-01

    The paper investigates influence and efficacy of laser therapy on pigmented and vascular cutaneous malformations by multispectral imaging technique. Parameter mapping of skin pigmented and vascular lesions and monitoring of the laser therapy efficacy are performed by multispectral imaging in wavelength range 450-700nm by scanning step - 10nm. Parameter maps of the oxyhemoglobin deoxyhemoglobin and melanin derived from the images are presented. Possibility of laser therapy efficacy monitoring by comparison of the parameter maps before and after laser treatment has been demonstrated. As both cutaneous pigmented and vascular malformations are commonly found lesions, the parameter mapping would be a valuable method to use routinely.

  14. Vector-lifting schemes based on sorting techniques for lossless compression of multispectral images

    NASA Astrophysics Data System (ADS)

    Benazza-Benyahia, Amel; Pesquet, Jean-Christophe

    2003-01-01

    In this paper, we introduce vector-lifting schemes which allow to generate very compact multiresolution representations, suitable for lossless and progressive coding of multispectral images. These new decomposition schemes exploit simultaneously the spatial and the spectral redundancies contained in multispectral images. When the spectral bands have different dynamic ranges, we improve dramatically the performances of the proposed schemes by a reversible histogram modification based on sorting permutations. Simulation tests carried out on real images allow to evaluate the performances of this new compression method. They indicate that the achieved compression ratios are higher than those obtained with currently used lossless coders.

  15. Angioscopic image-enhanced observation of atherosclerotic plaque phantom by near-infrared multispectral imaging at wavelengths around 1200 nm

    NASA Astrophysics Data System (ADS)

    Ishii, K.; Nagao, R.; Matsui, D.; Awazu, K.

    2015-02-01

    Spectroscopic techniques have been researched for intravascular diagnostic imaging of atherosclerotic plaque. Nearinfrared (NIR) light efficiently penetrates of biological tissues, and the NIR region contains the characteristic absorption range of lipid-rich plaques. The objective of this study is to observe atherosclerotic plaque using a NIR multispectral angioscopic imaging. Atherosclerotic plaque phantoms were prepared using a biological tissue model and bovine fat. For the study, we developed an NIR multispectral angioscopic imaging system with a halogen light, mercury-cadmiumtelluride camera, band-pass filters and an image fiber. Apparent spectral absorbance was obtained at three wavelengths, 1150, 1200 and 1300 nm. Multispectral images of the phantom were constructed using the spectral angle mapper algorithm. As a result, the lipid area, which was difficult to observe in a visible image, could be clearly observed in a multispectral image. Our results show that image-enhanced observation and quantification of atherosclerotic plaque by NIR multispectral imaging at wavelengths around 1200 nm is a promising angioscopic technique with the potential to identify lipid-rich plaques.

  16. Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis

    NASA Astrophysics Data System (ADS)

    Rottensteiner, Franz; Trinder, John; Clode, Simon; Kubik, Kurt

    In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of airborne laser scanner (ALS) data and multi-spectral images. For this purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic models for the probability mass assignments are validated and improved, and rules for tuning the parameters are discussed. The sensitivity of the results to the most important control parameters of the method is assessed. Further we evaluate the contributions of the individual cues used in the classification process to determine the quality of the results. Applying our method with a standard set of parameters on two different ALS data sets with a spacing of about 1 point/m 2, 95% of all buildings larger than 70 m 2 could be detected and 95% of all detected buildings larger than 70 m 2 were correct in both cases. Buildings smaller than 30 m 2 could not be detected. The parameters used in the method have to be appropriately defined, but all except one (which must be determined in a training phase) can be determined from meaningful physical entities. Our research also shows that adding the multi-spectral images to the classification process improves the correctness of the results for small residential buildings by up to 20%.

  17. Implementation of a neural network for multispectral luminescence imaging of lake pigment paints.

    PubMed

    Chane, Camille Simon; Thoury, Mathieu; Tournié, Aurélie; Echard, Jean-Philippe

    2015-04-01

    Luminescence multispectral imaging is a developing and promising technique in the fields of conservation science and cultural heritage studies. In this article, we present a new methodology for recording the spatially resolved luminescence properties of objects. This methodology relies on the development of a lab-made multispectral camera setup optimized to collect low-yield luminescence images. In addition to a classic data preprocessing procedure to reduce noise on the data, we present an innovative method, based on a neural network algorithm, that allows us to obtain radiometrically calibrated luminescence spectra with increased spectral resolution from the low-spectral resolution acquisitions. After preliminary corrections, a neural network is trained using the 15-band multispectral luminescence acquisitions and corresponding spot spectroscopy luminescence data. This neural network is then used to retrieve a megapixel multispectral cube between 460 and 710 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. The resulting data are independent from the detection chain of the imaging system (filter transmittance, spectral sensitivity of the lens and optics, etc.). As a result, the image cube provides radiometrically calibrated emission spectra with increased spectral resolution. For each pixel, we can thus retrieve a spectrum comparable to those obtained with conventional luminescence spectroscopy. We apply this method to a panel of lake pigment paints and discuss the pertinence and perspectives of this new approach.

  18. Semi-supervised segmentation of multispectral remote sensing image based on spectral clustering

    NASA Astrophysics Data System (ADS)

    Zhang, Xiangrong; Wang, Ting; Jiao, Licheng; Yang, Chun

    2009-10-01

    In this paper, a new multi-spectral remote sensing image segmentation method based on multi-parameter semi-supervised spectral clustering (STS3C) is proposed. Two types of instance-level constraints: must-link and cannot-link are incorporated into spectral cluster to construct semi-supervised spectral clustering in which the self-tuning parameter is applied to avoid the selection of the scaling parameter. Further, when STS3C is applied to multi-spectral remote sensing image segmentation, the uniform sampling technique combined with nearest neighbor rule is used to reduce the computation complexity. Segmentation results show that STS3C outperforms the semi-supervised spectral clustering with fixed parameter and the well-known clustering methods including k-means and FCM in multi-spectral remote sensing image segmentation.

  19. A simple multispectral imaging algorithm for detection of defects on red delicious apples

    USDA-ARS?s Scientific Manuscript database

    Purpose: A multispectral algorithm for detection and differentiation of defect and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used t...

  20. Exploiting multispectral imaging for non-invasive contamination assessment and mapping of meat samples.

    PubMed

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

    2016-12-01

    Recently, imaging and machine vision are gaining attention to food stakeholders since these are considered to be the emerging tools for food safety and quality assessment throughout the whole food chain. Herein, multispectral imaging, a surface chemistry sensor type, has been evaluated in terms of monitoring aerobically packaged beef filet spoilage at different storage temperatures (2, 8, and 15°C) and storage time. Spectral data acquired from the surface of meat samples (with/without background flora; +BF/-BF respectively) along with microbiological analysis. Qualitative analysis was employed for the discrimination of meat samples in two microbiological quality classes based on the values of total viable counts (TVC<2log10CFU/g and TVC>2log10CFU/g). Furthermore, a Support Vector Regression model was developed to provide quantitative estimations of microbial counts during storage. Results exhibit good performance with overall correct classification rate for the two quality classes ranging from 89.2% to 80.8% for model validation. The calculated regression results to an R-square of 0.98. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Automatic Object-Oriented Roundabouts Extrction from High Resolution Multispectral Images

    NASA Astrophysics Data System (ADS)

    Li, X.; Zhang, W.

    2017-09-01

    Road roundabouts, a typical class of road facilities to avoid collision, are generally not directed extracted in existing road extraction methods. This paper presents a novel four-step approach for automatic vegetated roundabout extractions from high resolution multispectral satellite images, which combines object-oriented extraction, Support Vector Machine (SVM) classification and spatial relationship estimation. Firstly, after proper preconditioning, the vegetated roundabouts are extracted by object-oriented extract algorithm in ENVI with rules that simultaneously taking area, roundness and vegetation index (NDVI) into consideration. After a certain number of experiments, the set of three items' thresholds can be found, which may stand as the general rules for vegetated roundabouts extraction in similar conditions. Next, the roads are classified using Support Vector Machine (SVM) and the outputs are several band shaped polygons. Then, the holes in road polygons will be detected by examining the topological relation in ArcGIS. Lastly, since the margin of extracted roundabout and the biggest detected hole may not strictly coincide, by comparing the distance between central points of both the extracted roundabout and the hole with the threshold, convincing determination can be made. The proposed automatic approach has been proved to have very high production accuracy that all above 85 % in each case of the test set, which is good enough for automatic vegetated roundabouts extraction from high resolution remote sensing images without manual interpretation.

  2. 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.

  3. Use Satellite Images and Improve the Accuracy of Hyperspectral Image with the Classification

    NASA Astrophysics Data System (ADS)

    Javadi, P.

    2015-12-01

    The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image. Mixed pixel classification or spectral unmixing, is a process that extracts the proportions of the pure components of each mixed pixel. This approach is called spectral unmixing. Hyper spectral images have higher spectral resolution than multispectral images. In this paper, pixel-based classification methods such as the spectral angle mapper, maximum likelihood classification and subpixel classification method (linear spectral unmixing) were implemented on the AVIRIS hyper spectral images. Then, pixel-based and subpixel based classification algorithms were compared. Also, the capabilities and advantages of spectral linear unmixing method were investigated. The spectral unmixing method that implemented here is an effective technique for classifying a hyperspectral image giving the classification accuracy about 89%. The results of classification when applying on the original images are not good because some of the hyperspectral image bands are subject to absorption and they contain only little signal. So it is necessary to prepare the data at the beginning of the process. The bands can be stored according to their variance. In bands with a high variance, we can distinguish the features from each other in a better mode in order to increase the accuracy of classification. Also, applying the MNF transformation on the hyperspectral images increase the individual classes accuracy of pixel based classification methods as well as unmixing method about 20 percent and 9 percent respectively.

  4. 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.

  5. Adaptive multispectral image processing for the detection of targets in terrain clutter

    NASA Astrophysics Data System (ADS)

    Hoff, Lawrence E.; Zeidler, James R.; Yerkes, Christopher R.

    1992-08-01

    In passive detection of small infrared targets in image data, we are faced with the difficult task of enhancing some characteristic of the target or signal while suppressing the clutter or background image noise. We reported that an effective means by which targets may be identified is to exploit characteristics which exist between scenes measured in different bands in the long wave infrared region of the electromagnetic spectrum. These methods are broadly termed multispectral techniques. In this paper we present a method by which a two- dimensional least-mean square adaptive filter is used to distinguish between target and clutter using multispectral techniques.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation

    PubMed Central

    Li, Ziwei; Suo, Jinli; Hu, Xuemei; Deng, Chao; Fan, Jingtao; Dai, Qionghai

    2017-01-01

    Combining spectral imaging with compressive sensing (CS) enables efficient data acquisition by fully utilizing the intrinsic redundancies in natural images. Current compressive multispectral imagers, which are mostly based on array sensors (e.g, CCD or CMOS), suffer from limited spectral range and relatively low photon efficiency. To address these issues, this paper reports a multispectral imaging scheme with a single-pixel detector. Inspired by the spatial resolution redundancy of current spatial light modulators (SLMs) relative to the target reconstruction, we design an all-optical spectral splitting device to spatially split the light emitted from the object into several counterparts with different spectrums. Separated spectral channels are spatially modulated simultaneously with individual codes by an SLM. This no-moving-part modulation ensures a stable and fast system, and the spatial multiplexing ensures an efficient acquisition. A proof-of-concept setup is built and validated for 8-channel multispectral imaging within 420~720 nm wavelength range on both macro and micro objects, showing a potential for efficient multispectral imager in macroscopic and biomedical applications. PMID:28128300

  11. Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation

    NASA Astrophysics Data System (ADS)

    Li, Ziwei; Suo, Jinli; Hu, Xuemei; Deng, Chao; Fan, Jingtao; Dai, Qionghai

    2017-01-01

    Combining spectral imaging with compressive sensing (CS) enables efficient data acquisition by fully utilizing the intrinsic redundancies in natural images. Current compressive multispectral imagers, which are mostly based on array sensors (e.g, CCD or CMOS), suffer from limited spectral range and relatively low photon efficiency. To address these issues, this paper reports a multispectral imaging scheme with a single-pixel detector. Inspired by the spatial resolution redundancy of current spatial light modulators (SLMs) relative to the target reconstruction, we design an all-optical spectral splitting device to spatially split the light emitted from the object into several counterparts with different spectrums. Separated spectral channels are spatially modulated simultaneously with individual codes by an SLM. This no-moving-part modulation ensures a stable and fast system, and the spatial multiplexing ensures an efficient acquisition. A proof-of-concept setup is built and validated for 8-channel multispectral imaging within 420~720 nm wavelength range on both macro and micro objects, showing a potential for efficient multispectral imager in macroscopic and biomedical applications.

  12. Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation.

    PubMed

    Li, Ziwei; Suo, Jinli; Hu, Xuemei; Deng, Chao; Fan, Jingtao; Dai, Qionghai

    2017-01-27

    Combining spectral imaging with compressive sensing (CS) enables efficient data acquisition by fully utilizing the intrinsic redundancies in natural images. Current compressive multispectral imagers, which are mostly based on array sensors (e.g, CCD or CMOS), suffer from limited spectral range and relatively low photon efficiency. To address these issues, this paper reports a multispectral imaging scheme with a single-pixel detector. Inspired by the spatial resolution redundancy of current spatial light modulators (SLMs) relative to the target reconstruction, we design an all-optical spectral splitting device to spatially split the light emitted from the object into several counterparts with different spectrums. Separated spectral channels are spatially modulated simultaneously with individual codes by an SLM. This no-moving-part modulation ensures a stable and fast system, and the spatial multiplexing ensures an efficient acquisition. A proof-of-concept setup is built and validated for 8-channel multispectral imaging within 420~720 nm wavelength range on both macro and micro objects, showing a potential for efficient multispectral imager in macroscopic and biomedical applications.

  13. Multispectral imaging reveals biblical-period inscription unnoticed for half a century

    PubMed Central

    Cordonsky, Michael; Levin, David; Moinester, Murray; Sass, Benjamin; Turkel, Eli; Piasetzky, Eli; Finkelstein, Israel

    2017-01-01

    Most surviving biblical period Hebrew inscriptions are ostraca—ink-on-clay texts. They are poorly preserved and once unearthed, fade rapidly. Therefore, proper and timely documentation of ostraca is essential. Here we show a striking example of a hitherto invisible text on the back side of an ostracon revealed via multispectral imaging. This ostracon, found at the desert fortress of Arad and dated to ca. 600 BCE (the eve of Judah’s destruction by Nebuchadnezzar), has been on display for half a century. Its front side has been thoroughly studied, while its back side was considered blank. Our research revealed three lines of text on the supposedly blank side and four "new" lines on the front side. Our results demonstrate the need for multispectral image acquisition for both sides of all ancient ink ostraca. Moreover, in certain cases we recommend employing multispectral techniques for screening newly unearthed ceramic potsherds prior to disposal. PMID:28614416

  14. 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.

  15. Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

    NASA Technical Reports Server (NTRS)

    Reitsema, H. J.; Mord, A. J.; Fraser, D.; Richard, H. L.; Speaker, E. E.

    1984-01-01

    Band-to-band coregistration of multispectral remote sensing images can be achieved by electronic signal processing techniques rather than by costly and difficult mechanical alignment. This paper describes the results of a study of the end-to-end performance of electronic registration. The software simulation includes steps which model the performance of the geometric calibration process, the instrument image quality, detector performance and the effects of achieving coregistration through image resampling. The image resampling step emulates the Pipelined Resampling Processor, a real-time image resampler. The study demonstrates that the electronic alignment technique produces multispectral images which are superior to those produced by an imager whose pixel geometry is accurate to 0.1 pixel rms. The implications of this approach for future earth observation programs are discussed.

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

    PubMed Central

    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

  17. Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

    NASA Technical Reports Server (NTRS)

    Reitsema, H. J.; Mord, A. J.; Fraser, D.; Richard, H. L.; Speaker, E. E.

    1984-01-01

    Band-to-band coregistration of multispectral remote sensing images can be achieved by electronic signal processing techniques rather than by costly and difficult mechanical alignment. This paper describes the results of a study of the end-to-end performance of electronic registration. The software simulation includes steps which model the performance of the geometric calibration process, the instrument image quality, detector performance and the effects of achieving coregistration through image resampling. The image resampling step emulates the Pipelined Resampling Processor, a real-time image resampler. The study demonstrates that the electronic alignment technique produces multispectral images which are superior to those produced by an imager whose pixel geometry is accurate to 0.1 pixel rms. The implications of this approach for future earth observation programs are discussed.

  18. 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.

  19. 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

  20. 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.

  1. 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).

  2. 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).

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

    SciTech Connect

    Bhateja, Vikrant E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong E-mail: nhuongld@hus.edu.vn

    2016-07-15

    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).

  4. Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images

    PubMed Central

    Larobina, Michele; Murino, Loredana; Cervo, Amedeo; Alfano, Bruno

    2015-01-01

    The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction. PMID:26583131

  5. Optimization of wavelengths sets for multispectral reflectance imaging of rat olfactory bulb activation in vivo

    NASA Astrophysics Data System (ADS)

    Renaud, Rémi; Bendahmane, Mounir; Chery, Romain; Martin, Claire; Gurden, Hirac; Pain, Frederic

    2012-06-01

    Wide field multispectral imaging of light backscattered by brain tissues provides maps of hemodynamics changes (total blood volume and oxygenation) following activation. This technique relies on the fit of the reflectance images obtain at two or more wavelengths using a modified Beer-Lambert law1,2. It has been successfully applied to study the activation of several sensory cortices in the anesthetized rodent using visible light1-5. We have carried out recently the first multispectral imaging in the olfactory bulb6 (OB) of anesthetized rats. However, the optimization of wavelengths choice has not been discussed in terms of cross talk and uniqueness of the estimated parameters (blood volume and saturation maps) although this point was shown to be crucial for similar studies in Diffuse Optical Imaging in humans7-10. We have studied theoretically and experimentally the optimal sets of wavelength for multispectral imaging of rodent brain activation in the visible. Sets of optimal wavelengths have been identified and validated in vivo for multispectral imaging of the OB of rats following odor stimulus. We studied the influence of the wavelengths sets on the magnitude and time courses of the oxy- and deoxyhemoglobin concentration variations as well as on the spatial extent of activated brain areas following stimulation. Beyond the estimation of hemodynamic parameters from multispectral reflectance data, we observed repeatedly and for all wavelengths a decrease of light reflectance. For wavelengths longer than 590 nm, these observations differ from those observed in the somatosensory and barrel cortex and question the basis of the reflectance changes during activation in the OB. To solve this issue, Monte Carlo simulations (MCS) have been carried out to assess the relative contribution of absorption, scattering and anisotropy changes to the intrinsic optical imaging signals in somatosensory cortex (SsC) and OB model.

  6. 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

    2012-11-05

    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.

  7. A multispectral photon-counting double random phase encoding scheme for image authentication.

    PubMed

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

    2014-05-20

    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.

  8. 3D tensor-based blind multispectral image decomposition for tumor demarcation

    NASA Astrophysics Data System (ADS)

    Kopriva, Ivica; Peršin, Antun

    2010-03-01

    Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

  9. 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

  10. 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.

  11. 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.

  12. Evaluating color performance of whole-slide imaging devices by multispectral-imaging of biological tissues

    NASA Astrophysics Data System (ADS)

    Saleheen, Firdous; Badano, Aldo; Cheng, Wei-Chung

    2017-03-01

    The color reproducibility of two whole-slide imaging (WSI) devices was evaluated with biological tissue slides. Three tissue slides (human colon, skin, and kidney) were used to test a modern and a legacy WSI devices. The color truth of the tissue slides was obtained using a multispectral imaging system. The output WSI images were compared with the color truth to calculate the color difference for each pixel. A psychophysical experiment was also conducted to measure the perceptual color reproducibility (PCR) of the same slides with four subjects. The experiment results show that the mean color differences of the modern, legacy, and monochrome WSI devices are 10.94+/-4.19, 22.35+/-8.99, and 42.74+/-2.96 ▵E00, while their mean PCRs are 70.35+/-7.64%, 23.06+/-14.68%, and 0.91+/-1.01%, respectively.

  13. Analysis of variograms with various sample sizes from a multispectral image

    USDA-ARS?s Scientific Manuscript database

    Variograms play a crucial role in remote sensing application and geostatistics. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100 X 100 pixel subset was chosen from an aerial multispectral image which contained three wavebands, green, ...

  14. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

  18. Distant Determination of Bilirubin Distribution in Skin by Multi-Spectral Imaging

    NASA Astrophysics Data System (ADS)

    Saknite, I.; Jakovels, D.; Spigulis, J.

    2011-01-01

    For mapping the bilirubin distribution in bruised skin the multi-spectral imaging technique was employed, which made it possible to observe temporal changes of the bilirubin content in skin photo-types II and III. The obtained results confirm the clinical potential of this technique for skin bilirubin diagnostics.

  19. Multispectral imaging and sensing techniques for chlorophyll and nitrate determination in potato plants

    NASA Astrophysics Data System (ADS)

    Borhan, Md. Saidul

    Two laboratory based multispectral imaging systems were developed. The image features from color (Red, Green, and blue) and multispectral bands (550, 710, and 810 nm) were evaluated in predicting chlorophyll and nitrate contents of potato leaves grown in the greenhouse. Histogram based image features were extracted from normalized 'r' and 'g' and multispectral band images. Normalized 'r' feature ('r' average energy) was found negatively correlated (R2 = 0.90) with chlorophyll contents of leaves. Multispectral band image features were found to be linearly correlated (R2 = 0.95) to chlorophyll contents. Average prediction accuracies varied from 90.52 to 92.36% for normalized color and multispectral band image features. Average and maximum prediction accuracies were 75.15% and 99.01%, respectively, for petiole nitrate prediction using normalized 'r' image feature. Two portable, handheld multispectral spectroscopic sensing systems were also developed and evaluated to predict chlorophyll content of potato leaves. The first prototype (walking type) sensor was designed for use in outdoor condition and the second prototype (closed type) sensor was designed for use in both indoor and outdoor condition. A signal-processing algorithm was developed to process the acquired signals and to obtain the second derivative of the spectral signature. In the preliminary test, the walking-type sensor resulted in an average prediction accuracy of 83.78% using the hybrid NN model. The linear correlation between actual and predicted chlorophyll was found to be 0.95. The hybrid neural network model resulted in a correlation coefficient and average prediction accuracy of 0.93 and 77.73%, respectively, using a closed-type sensor. The walking-type multispectral sensing system was improved and evaluated to predict the chlorophyll content of potato leaves in the field. To make the sensor handy and compact, a handheld computer and associated hardware were incorporated into the design. A field test

  20. 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.

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

    NASA Astrophysics Data System (ADS)

    Lemeshewsky, George P.

    2003-08-01

    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.

  2. 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.

  3. 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.

  4. 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.

  5. Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter.

    PubMed

    Bouhrara, Mustapha; Bonny, Jean-Marie; Ashinsky, Beth G; Maring, Michael C; Spencer, Richard G

    2017-01-01

    Denoising of magnetic resonance (MR) images enhances diagnostic accuracy, the quality of image manipulations such as registration and segmentation, and parameter estimation. The first objective of this paper is to introduce a new, high-performance, nonlocal filter for noise reduction in MR image sets consisting of progressively-weighted, that is, multispectral, images. This filter is a multispectral extension of the nonlocal maximum likelihood filter (NLML). Performance was evaluated on synthetic and in-vivo T2 - and T1 -weighted brain imaging data, and compared to the nonlocal-means (NLM) and its multispectral version, that is, MS-NLM, and the nonlocal maximum likelihood (NLML) filters. Visual inspection of filtered images and quantitative analyses showed that all filters provided substantial reduction of noise. Further, as expected, the use of multispectral information improves filtering quality. In addition, numerical and experimental analyses indicated that the new multispectral NLML filter, MS-NLML, demonstrated markedly less blurring and loss of image detail than seen with the other filters evaluated. In addition, since noise standard deviation (SD) is an important parameter for all of these nonlocal filters, a multispectral extension of the method of maximum likelihood estimation (MLE) of noise amplitude is presented and compared to both local and nonlocal MLE methods. Numerical and experimental analyses indicated the superior performance of this multispectral method for estimation of noise SD.

  6. Winter wheat nutrition diagnosis under different N treatments based on multispectral images and remote sensing

    NASA Astrophysics Data System (ADS)

    Zhao, Ruijiao; Li, Minzan; Li, Shuqiang; Ding, Yongjun

    2010-11-01

    In order to rapidly and accurately acquire winter wheat growing information and nitrogen content, a non-destructive testing method was developed combined with multi-spectral imaging technique and remote sensing technology to research wheat growing and nutrition status. Firstly, a 2-CCD multi-spectral image collecting platform was developed to acquire visible image and NIR image synchronously, meanwhile, the canopy spectral reflectance and the nitrogen content of wheat leaves were measured and analyzed to research the characteristics of the canopy spectral reflectance. Secondly, using calibration panels the experiential linear calibration model was established between image gray value and spectral reflectance. Thirdly, NIR image was processed to segment wheat canopy from soil and then gray value of wheat leaves was achieved by image processing of Red, Green, and Blue channels. Finally, the gray value of wheat leaves was transformed into spectral reflectance by aforementioned experiential linear model, and the vegetation index were calculated and analyzed to research the winter wheat growing and nitrogen content status. Experiment results showed that it was reasonable to diagnose nitrogen content of winter wheat based on multi-spectral imaging system and experiential linear model. There existed remarkable correlation between vegetation index (NDVI, GNDVI) and nitrogen content of winter wheat, and the correlation coefficients (R2 ) were 0.633 and 0.6.

  7. Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation.

    PubMed

    Kazakeviciute, Agne; Ho, Chris Jun Hui; Olivo, Malini

    2016-09-01

    The aim of this study is to solve a problem of denoising and artifact removal from in vivo multispectral photoacoustic imaging when the level of noise is not known a priori. The study analyzes Wiener filtering in Fourier domain when a family of anisotropic shape filters is considered. The unknown noise and signal power spectral densities are estimated using spectral information of images and the autoregressive of the power 1 ( AR(1)) model. Edge preservation is achieved by detecting image edges in the original and the denoised image and superimposing a weighted contribution of the two edge images to the resulting denoised image. The method is tested on multispectral photoacoustic images from simulations, a tissue-mimicking phantom, as well as in vivo imaging of the mouse, with its performance compared against that of the standard Wiener filtering in Fourier domain. The results reveal better denoising and fine details preservation capabilities of the proposed method when compared to that of the standard Wiener filtering in Fourier domain, suggesting that this could be a useful denoising technique for other multispectral photoacoustic studies.

  8. Development of filter exchangeable 3CCD camera for multispectral imaging acquisition

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

    There are a lot of methods to acquire multispectral images. Dynamic band selective and area-scan multispectral camera has not developed yet. This research focused on development of a filter exchangeable 3CCD camera which is modified from the conventional 3CCD camera. The camera consists of F-mounted lens, image splitter without dichroic coating, three bandpass filters, three image sensors, filer exchangeable frame and electric circuit for parallel image signal processing. In addition firmware and application software have developed. Remarkable improvements compared to a conventional 3CCD camera are its redesigned image splitter and filter exchangeable frame. Computer simulation is required to visualize a pathway of ray inside of prism when redesigning image splitter. Then the dimensions of splitter are determined by computer simulation which has options of BK7 glass and non-dichroic coating. These properties have been considered to obtain full wavelength rays on all film planes. The image splitter is verified by two line lasers with narrow waveband. The filter exchangeable frame is designed to make swap bandpass filters without displacement change of image sensors on film plane. The developed 3CCD camera is evaluated to application of detection to scab and bruise on Fuji apple. As a result, filter exchangeable 3CCD camera could give meaningful functionality for various multispectral applications which need to exchange bandpass filter.

  9. [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

  10. [Multispectral remote sensing image denoising based on non-local means].

    PubMed

    Liu, Peng; Liu, Ding-Sheng; Li, Guo-Qing; Liu, Zhi-Wen

    2011-11-01

    The non-local mean denoising (NLM) exploits the fact that similar neighborhoods can occur anywhere in the image and can contribute to denoising. However, these current NLM methods do not aim at multichannel remote sensing image. Smoothing every band image separately will seriously damage the spectral information of the multispectral image. Then the authors promote the NLM from two aspects. Firstly, for multispectral image denoising, a weight value should be related to all channels but not only one channel. So for the kth band image, the authors use sum of smoothing kernel in all bands instead of one band. Secondly, for the patch whose spectral feature is similar to the spectral feature of the central patch, its weight should be larger. Bringing the two changes into the traditional non-local mean, a new multispectral non-local mean denoising method is proposed. In the experiments, different satellite images containing both urban and rural parts are used. For better evaluating the performance of the different method, ERGAS and SAM as quality index are used. And some other methods are compared with the proposed method. The proposed method shows better performance not only in ERGAS but also in SAM. Especially the spectral feature is better reserved in proposed NLM denoising.

  11. 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.

  12. 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.

  13. Active Contours for Multispectral Images With Non-Homogeneous Sub-Regions

    DTIC Science & Technology

    2005-09-16

    green , and blue. Hyperspectral images, used in remote sensing, are other examples of multispectral im- ages. A set of images, measured by physically...method 1, (b) method 2, (c) proposed method The green solid line of all three graphs presents the same statistics measured within class 2 in the...segmentation result. As the green solid line exists closer to the blue solid line, it presents better result. In figure 8.7(a), the dotted green line with

  14. Combining transverse field detectors and color filter arrays to improve multispectral imaging systems.

    PubMed

    Martínez, Miguel A; Valero, Eva M; Hernández-Andrés, Javier; Romero, Javier; Langfelder, Giacomo

    2014-05-01

    This work focuses on the improvement of a multispectral imaging sensor based on transverse field detectors (TFDs). We aimed to achieve a higher color and spectral accuracy in the estimation of spectral reflectances from sensor responses. Such an improvement was done by combining these recently developed silicon-based sensors with color filter arrays (CFAs). Consequently, we sacrificed the filter-less full spatial resolution property of TFDs to narrow down the spectrally broad sensitivities of these sensors. We designed and performed several experiments to test the influence of different design features on the estimation quality (type of sensor, tunability, interleaved polarization, use of CFAs, type of CFAs, number of shots), some of which are exclusive to TFDs. We compared systems that use a TFD with systems that use normal monochrome sensors, both combined with multispectral CFAs as well as common RGB filters present in commercial digital color cameras. Results showed that a system that combines TFDs and CFAs performs better than systems with the same type of multispectral CFA and other sensors, or even the same TFDs combined with different kinds of filters used in common imaging systems. We propose CFA+TFD-based systems with one or two shots, depending on the possibility of using longer capturing times or not. Improved TFD systems thus emerge as an interesting possibility for multispectral acquisition, which overcomes the limited accuracy found in previous studies.

  15. Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

    NASA Technical Reports Server (NTRS)

    Blonksi, Slawomir; Gasser, Gerald; Russell, Jeffrey; Ryan, Robert; Terrie, Greg; Zanoni, Vicki

    2001-01-01

    Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.

  16. Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

    NASA Technical Reports Server (NTRS)

    Blonksi, Slawomir; Gasser, Gerald; Russell, Jeffrey; Ryan, Robert; Terrie, Greg; Zanoni, Vicki

    2001-01-01

    Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.

  17. 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

  18. [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.

  19. Authenticity detection in image classification

    NASA Astrophysics Data System (ADS)

    Nova, Luis C.; Passos, Emmanuel P. L.

    1992-09-01

    Famous artists' paintings, in general, allow for a large number of forgeries. In the work of a great Brazilian painter, Candido Portinari, we try to detect fake works through their image. To reach classifying results we must extract from digitalized images features that distinguish Portinari's original paintings from the false ones. So, it has been noted that the degree of variation of gray tones in a brush stroke reflects each painter's particular style on the painting. It is a feature that can be easily detected by the power spectrum of the detail image. However, as power spectra have large amounts of data, we use just some significant values of them for the classification. These selected pixels of the spectrum image are in a line which is perpendicular to the direction the brush passed, i.e., they indicate the variation of gray tones in the brush stroke. The data described previously are used as input for further classification by a backpropagation neural network. This neural net was exhaustively trained and has topology and parameters appropriate to the problem. Two output units indicate the major result: original or false.