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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery II"

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Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery

Nonlinear signal contamination effects for gaseous plume det...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Theiler, James Foy, Bernard R. Fraser, Andrew M. Los Alamos Natl Lab Los Alamos NM 87544 USA

ISBN: (纸本)0819462896

When a matched filter is used for detecting a weak target in a cluttered background (such as a gaseous plume in a hyperspectral image), it is important that the background clutter be well-characterized. A statistical characterization can be obtained from the off-plume pixels of a hyperspectral image, but if on-plume pixels are inadvertently included, then that background characterization will be contaminated. In broad area search scenarios, where detection is the central aim, it is by definition unknown which pixels in the scene are on-plume, so some contamination is inevitable. In general, the contaminated background degrades the ability of the matched-filter to detect that signal. This could be a practical problem in plume detection. A linear analysis suggests that the effect is limited, and actually vanishes in some cases. In this study, we take into account the Beer's Law nonlinearity of plume absorption, and we investigate the effect of that nonlinearity on the signal contamination.

关键词： matched filter hyperspectral imagery adaptive signal detection clutter signal contamination

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Change detection in hyperspectral imagery using temporal principal components

Change detection in hyperspectral imagery using temporal pri...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Ortiz-Rivera, Vanessa Velez-Reyes, Miguel Roysam, Badrinath Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Mayaguez PR 00682 USA Rensselaer Polytech Inst Ctr Subsurface Sensing & Imaging Syst Troy NY 12181 USA

ISBN: (纸本)0819462896

Change detection is the process of automatically identifying and analyzing regions that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. Change detection in sequences of hyperspectral images is complicated by the fact that change can occur in the temporal and/or spectral domains. This work studies the use of Temporal Principal Component Analysis (TPCA) for change detection in multi/hyperspectral images. Two additional methods were implemented in order to compare its results with TPCA. These were: Image Differencing and Conventional Principal Component Analysis. Experimental results using phantom hyperspectral imagery taken with Surface Optics SOC-700 hyperspectral camera are presented. The algorithms were implemented using Matlab, and their performance is compared in terms of false alarms, missed changes and overall error. Results show that the performance of TPCA was the best, obtaining the smallest percentages of error, missed changes, and false alarms using global or local threshold. TPCA with local threshold gave the best performance.

关键词： hyperspectral imagery change detection principal components

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Sparse linear filters for detection and classification in hyperspectral imagery

Sparse linear filters for detection and classification in hy...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Theiler, James Glocer, Karen Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM USA

ISBN: (纸本)0819462896

We investigate the use of convex optimization to identify sparse linear filters in hyperspectral. imagery. A linear filter is sparse if a large fraction of its coefficients are zero. A sparse linear filter can be advantageous because it only needs to access a subset of the available spectral channels, and it can be applied to high-dimensional data more cheaply than a standard linear detector. Finding good sparse filters is nontrivial because there is a combinatorially large number of discrete possibilities from which to choose the optimal subset of nonzero coefficients. But, by converting the optimality criterion into a convex loss function, and by employing an L1 penalty, one can obtain sparse solutions that are globally optimal. We investigate the performance of these sparse filters as a function of their sparsity, and compare the convex optimization approach with more traditional alternatives for feature selection. The methodology is applied both to the adaptive matched filter for weak signal detection, and to the Fisher linear discriminant for terrain categorization.

关键词： matched filter hyperspectral imagery adaptive signal detection

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Linearly constrained band selection for hyperspectral imagery

Linearly constrained band selection for hyperspectral imager...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Wang, Su Chang, Chein-, I Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819462896

Linearly constrained adaptive beamforming has been used to design hyperspectral target detection algorithms such as constrained energy minimization (CEM) and linearly constrained minimum variance (LCMV). It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper investigates this idea and further uses it to develop a new approach to band selection, referred to as linear constrained band selection (LCBS) for hyperspectral imagery. It interprets a band image as a desired target signature while considering other band images as unknown signatures. With this interpretation, the proposed LCBS linearly constrains a band image while also minimizing band correlation or dependence caused by other band images. As a result, two different methods referred to as Band Correlation Minimization (BCM) and Band Correlation Constraint (BCC) can be developed for band selection. Such LCBS allows one to select desired bands for data analysis. In order to determine the number of bands required to select, p, a recently developed concept, called virtual dimensionality (VD) is used to estimate the p. Once the p is determined, a set of p desired bands can be selected by LCBS. Finally, experiments are conducted to substantiate the proposed LCBS.

关键词： Target detection hyperspectral imaging Algorithm development Image processing Detection and tracking algorithms Linear filtering Image filtering Optical filters hyperspectral target detection Principal component analysis

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Discriminant analysis with nonparametric estimates for subpixel detection of 3D objects in hyperspectral imagery

Discriminant analysis with nonparametric estimates for subpi...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Liu, Yong Healey, Glenn Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

ISBN: (纸本)0819462896

The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The use of subspace models for targets and backgrounds allows detection that is invariant to changing environmental conditions. The non-Gaussian behavior of target and background distribution residuals complicates the development of subspace-based detection methods. In this paper, we use discriminant analysis for feature extraction for separating subpixel 3D objects from cluttered backgrounds. The nonparametric estimation of distributions is used to establish the statistical models using the length and direction of residuals. Candidate subspaces are then evaluated to maximize their discriminatory power which is measured between estimated distributions of targets and backgrounds. In this context, a likelihood ratio test is used based on background and mixed statistics for subpixel detection. The detection algorithm is evaluated for HYDICE images and a number of images simulated using DIRSIG under a variety of conditions. The experimental results demonstrate accurate detection performance on these data sets.

关键词： Statistical analysis hyperspectral imaging Feature extraction Target detection 3D acquisition 3D image processing 3D modeling Data modeling Detection and tracking algorithms Environmental sensing

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Perceptual display strategies of hyperspectral imagery based on PCA and ICA

Perceptual display strategies of hyperspectral imagery based...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Zhang, Hongqin Messinger, David W. Montag, Ethan D. Rochester Inst Technol Munsell Color Sci Lab 54 Lomb Mem Dr Rochester NY 14623 USA Rochester Inst Technol Digital Imaging & Remote Sensing Lab Rochester NY 14623 USA

ISBN: (纸本)0819462896

This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality in order to make the data more amenable to visualization in three-dimensional color space. In addition, these two methods were chosen because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based visualization strategies were then explored by mapping the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCrCb, and YUV. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes.

关键词： hyperspectral imagery visualization opponent color spaces human color vision

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Error rates of unmixed hyperspectral imagery

Error rates of unmixed hyperspectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Winter, Michael E. Hawaii Inst Geophys & Planetol Sch Ocean & Earth Sci & Technol Honolulu HI 96822 USA

ISBN: (纸本)0819462896

When analyzing a hyperspectral image using the linear mixture model, one makes a variety of assumptions relating to the distribution of error and the underlying mixture model. In order to test the validity of these assumptions, a simple model of hyperspectral data is examined. Generally, simple linear unmixing is performed assuming that sensor error rates are the same for each band. This assumption is violated quite easily when unmixing reflectance data. Assuming a perfect sensor, image data that perfectly obeys the linear mixture model, and perfectly known end-member spectra, the error rate for least squares linear unmixing is determinable using a simple formula. When data is transformed into reflectance, the error rates for the unmixed image increases by a significant factor due to the poor statistical normalization of the resulting data. As a means of mitigating error in unmixed imagery, two alternative unmixing methods are examined: non-negative least squares, and total least squares. Non-negative least squares can be shown to significantly outperform simple least squares, while total least squares behaves pathologically. Unmixing hyperspectral images inherently transfers error from the original hyperspectral image to the unmixed fraction plane image. Care should be taken when unmixing, so that this error is known and minimized.

关键词： image analysis image unmixing spectral end-member error analysis total least squares

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Sample spectral correlation-based measures for subpixels and mixed pixels in real hyperspectral imagery

Sample spectral correlation-based measures for subpixels and...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Liu, Weimin Chang, Chein-, I Univ Maryland Baltimore Cty Remote Sensing Signal & Image Proc Lab Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)0819462896

A hyperspectral imaging sensor images a scene using hundreds of contiguous spectral channels to uncover many substances that cannot be resolved by multspectral sensors with tens of discrete spectral channels. Many spectral measures used for target discrimination and identification in hyperspectral imagery have been derived directly from multsispectral imagery rather than from a hyperspectral imagery viewpoint. This paper demonstrates that on many occasions such spectral measures are generally not effective when it is applied to real hyperspectral data for discrimination and identification due to the fact that they do not take into account the very high sample spectral correlation (SSC) provided by hyperspectral sensors. In order to address this issue, two approaches, referred to as a priori sample spectral correlation (PR-SSC) and a posteriori SSC (PSSSC) arc developed to account for spectral variability within real data to achieve better target discrimination and identification. While the former can be used to derive a family of a priori hyperspectral measures via orthogonal subspace projection (OSP) to eliminate interfering effects caused by undesired signatures, the latter results in a family of a posteriori hyperspectral measures that include sample covariance/correlation matrix as a posteriori information to increase ability in discrimination and identification. Interestingly, some well-known measures such as Euclidean distance (ED) and spectral angle mapper (SAM) can be shown to be special cases of the proposed PR-SSC and PS-SSC hyperspectral measures.

关键词： Euclidean distance (ED) orthogonal projection divergence (OPD) orthogonal subspace projection (OSP) pixel level-based spectral measures a posteriori sample spectral correlation (PS-SSC) a priori sample spectral correlation (PR-SSC) sample spectral correlation (SSC) spectral angle mapper (SAM) spectral information divergence (SID)

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A classification approach and comparison to other object identification algorithms for hyperspectral imagery

A classification approach and comparison to other object ide...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Mayer, R. Antoniades, J. Baumback, M. Chester, D. Edwards, J. Goldstein, A. Haas, D. Henderson, S. BAE Syst ATI Washington DC 20037 USA

ISBN: (纸本)0819462896

This study adapts a variety of techniques derived from multi-spectral image classification to find objects amid cluttered backgrounds in hyperspectral imagery. This study quantitatively compares the algorithms against a standard object search, the matched filter (MF) and recently developed object detector, Adaptive Cosine Estimator (ACE). These object searches require calculating the Mahalanobis distance between the average object spectral signature and the test pixel spectrum and needs the computation of a covariance matrix. The covariance matrix is generated using the entire image (Whitened Euclidean Distance, WED) or using pixels associated with the object (Maximum Likelihood Classifier, MLC). The latter computation requires a relatively large number of pixels to generate a non-singular, accurate covariance matrix. Regularizing object pixels via optimally mixing (likelihood maximization) diagonal, object, and entire image covariance matrices to generate the object covariance matrix estimate. This approximation is called the Regularized Maximum Likelihood Classifier (RMLC). The object searches MF, ACE, WED, MLC, and RMLC were applied to visible/near IR data collected from forest and desert environments. This study searched for objects using object signatures and covariance matrices taken directly from the scene and from statistically transformed object signatures and covariance matrices from another time. This study found a substantial reduction in the number of false alarms (factor of 10 to 1000) using WED, ACE, RMLC relative to MF searches for the two independent data collects. The regularization of in-scene and transformed covariance matrices substantially reduced false alarms relative to using unprocessed covariance matrices. This study adds simple, high performing algorithms to the object search arsenal.

关键词： target detection object identification hyperspectral imagery matched filters

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Design and modelling of spectral-thermal unmixing targets for airborne hyperspectral imagery

Design and modelling of spectral-thermal unmixing targets fo...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery Xii

作者： Clare, Phil Def Sci & Technol Lab Farnborough GU14 0LX Hants England

ISBN: (纸本)0819462896

Techniques to determine the proportions of constituent materials within a single pixel spectrum are well documented in the reflective (0.4-2.5 mu m) domain. The same capability is also desirable for the thermal (7-14 mu m) domain, but is complicated by the thermal contributions to the measured spectral radiance. Atmospheric compensation schemes for the thermal domain have been described along with methods for estimating the spectral emissivity from a spectral radiance measurement and hence the next stage to be tackled is the unmixing of thermal spectral signatures. In order to pursue this goal it is necessary to collect data of well-calibrated targets which will expose the limits of the available techniques and enable more robust methods to be designed. This paper describes the design of a set of ground targets for an airborne hyperspectral imager, which will test the effectiveness of available methods. The set of targets include panels to explore a number of difficult scenarios such as isothermal (different materials at identical temperature), isochromal (identical materials, but at differing temperatures), thermal adjacency and thermal point sources. Practical fabrication issues for heated targets and selection of appropriate materials are described. Mathematical modelling of the experiments has enabled prediction of at-sensor measured radiances which are used to assess the design parameters. Finally, a number of useful lessons learned during the fielding of these actual targets are presented to assist those planning future trials of thermal hyperspectral sensors.

关键词： thermal hyperspectral targets unmixing emissivity

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