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Restoration of hyperspectral imagery

Restoration of hyperspectral imagery

引用

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

作者： Umana-Diaz, Alejandra Velez-Reyes, Miguel Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc POB 9048 Mayaguez PR 00681 USA

ISBN: (纸本)0819462896

In hyperspectral imaging, the quality of the collected spectral signatures can be degraded by blurring due to the channel weighting function of the imaging spectrometer. In this work, we are investigating reconstruction techniques to enhance salient features and remove degradation effects in measured spectra to assist in subsequent machine analysis. Here preliminary work is presented showing spectral restoration using simulated data and real data of the AVIRIS NW Indian Pines hyperspectral image using different restoration algorithms. The restored AVIRIS image was classified and the classification accuracy was used to assess the usefulness of the restoration process. All the methods gave comparable results with the Jansson method giving slightly higher classification accuracy.

关键词： hyperspectral imaging spectral restoration hyperspectral image restoration

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A comparison of algorithms to compute the positive matrix factorization and their application to unsupervised unmixing

A comparison of algorithms to compute the positive matrix fa...

引用

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

作者： Masalmah, Yahya M. Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Ctr Subsurface Sensing & Imaging Syst Mayaguez Campus POB 9042 Mayaguez PR 00681 USA

ISBN: (纸本)0819462896

This paper presents a comparison of different algorithms to compute the constraint positive matrix factorization and their application to the unsupervised unmixing problem. We study numerical methods based on the Gauss-Newton algorithm, the Seung-Lee approach, the Gauss-Seidel algorithm, and penalty methods. Preliminary results using a Hyperion image from southwestern Puerto Rico presented. algorithms will be compared in terms of their convergence performance, and quality of the results.

关键词： hyperspectral imagery spectral unmixing Positive matrix factorization Gauss-Newton method

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Fukunaga-Koontz Transform based dimensionality reduction for hyperspectral imagery

Fukunaga-Koontz Transform based dimensionality reduction for...

引用

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

作者： Ochilov, S. Alam, M. S. Bal, A. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)0819462896

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space. where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

关键词： Fukunaga-Koontz Transform hyperspectral imagery dimensionality reduction feature classification

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Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery

Nonlinear signal contamination effects for gaseous plume det...

引用

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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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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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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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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