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

共 408 条记录，以下是61-70 订阅

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Parameter Estimation for Support Vector Anomaly Detection in hyperspectral imagery

Parameter Estimation for Support Vector Anomaly Detection in...

引用

Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XViiI

作者： Meth, Reuven Ahn, James Banerjee, Amit Juang, Radford Burlina, Philippe SAIC Co SET Corp Greenbelt MD 20770 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9780819490681

hyperspectral Image (HSI) anomaly detectors typically employ local background modeling techniques to facilitate target detection from surrounding clutter. Global background modeling has been challenging due to the multi-modal content that must be automatically modeled to enable target/background separation. We have previously developed a support vector based anomaly detector that does not impose an a priori parametric model on the data and enables multi-modal modeling of large background regions with inhomogeneous content. Effective application of this support vector approach requires the setting of a kernel parameter that controls the tightness of the model fit to the background data. Estimation of the kernel parameter has typically considered Type I / false-positive error optimization due to the availability of background samples, but this approach has not proven effective for general application since these methods only control the false alarm level, without any optimization for maximizing detection. Parameter optimization with respect to Type ii / false-negative error has remained elusive due to the lack of sufficient target training exemplars. We present an approach that optimizes parameter selection based on both Type I and Type ii error criteria by introducing outliers based on existing hypercube content to guide parameter estimation. The approach has been applied to hyperspectral imagery and has demonstrated automatic estimation of parameters consistent with those that were found to be optimal, thereby providing an automated method for general anomaly detection applications.

关键词： HSI hyperspectral Anomaly Detection Parameter Estimation SVM SVDD

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Tri-lateral Filter on multispectral imagery for Classification and Segmentation

Tri-lateral Filter on Multispectral Imagery for Classificati...

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

作者： Sun, Weihua Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9780819486226

In this paper, we present a new approach to filtering high spatial resolution multispectral (MSI) or hyperspectral imagery (HSI) for the purpose of classification and segmentation. Our approach is inspired by the bilateral filtering method(1) that smooths images while preserving important edges for gray-scale and color images. To achieve a similar goal for MSI/HSI, we build a nonlinear tri-lateral filter that takes into account both spatial and spectral similarities. Our approach works on a pixel by pixel basis;the spectrum of each pixel in the filtered image is the combination of the spectra of its adjacent pixels in the original image weighted by the three factors: geometric closeness, spectral Euclidean distance and spectral angle separation. The approach reduces small clutter across the image while keeping edges with strong contrast. The improvement of our method is that we use both spectral intensity differences together with spectral angle separation as the closeness metric, thus preserving edges caused both by material as well as by similar materials with intensity differences. A k-means classifier is applied to the filtered image and the results show our approach can produce a much less cluttered class map. Results will be shown using imagery from the Digital Globe Worldview-2 multispectral sensor and the HYDICE hyperspectral sensor. This approach could also be expanded to facilitate feature extraction from MSI/HSI.

关键词： Image filtering Optical filters Image segmentation Nonlinear filtering Image classification Sensors Buildings Image processing multispectral imaging Vegetation

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hyperspectral target detection using Independent Component Analysis based linear mixture model

Hyperspectral target detection using Independent Component A...

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

作者： Sarigul, E. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466877

This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to discriminate statistically independent sources to determine end-members in a given dataset as well as their corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that correspond to the target class. The performance of the developed algorithm has been evaluated with several real life hyperspectral image datasets.

关键词： Independent Component Analysis target detection hyperspectral imagery spectral angle mapping unmixing hyperspectral dataindependent

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A full algorithm to compute the constrained positive matrix factorization and its application in unsupervised unmixing of hyperspectral imagery

A full algorithm to compute the constrained positive matrix ...

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

作者： Masalmah, Yahya M. Velez-Reyes, Miguel Univ Turabo Dept Elect & Comp Engn POB 3030 Gurabo PR 00778 USA Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)9780819471574

This paper presents a full algorithm to compute the solution for the unsupervised unmixing problem based on the positive matrix factorization. The algorithm estimates the number of endmembers as the rank of the matrix. The algorithm has an initialization stage using the SVD subset selection algorithm. Testing and validation with real and simulated data show, the effectiveness of the method. Application of the approach to environmental remote sensing is shown.

关键词： hyperspectral imagery spectral unmixing abundance estimation endmember determination iterative methods initialization positive matrix factorization

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Onboard Cubesat Data Processing for hyperspectral Detection of Chemical Plumes 24

Onboard Cubesat Data Processing for Hyperspectral Detection ...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Theiler, James Foy, Bernard R. Safi, Claira Love, Steven P. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

We describe the development and implementation of plume detection algorithms under severe bandwidth and processing constraints imposed by a CubeSat architecture. In particular, two ideas will be presented: one employs onboard processing to reduce the data that is downlinked, and one employs the Sparse Matrix Transform (SMT) to speed up the onboard computation of an approximate Mahalanobis distance.

关键词： cubesat satellite hyperspectral imagery algorithm plume detection sparse matrix transform Mahalanobis distance matched filter adaptive coherence estimator

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Automatic ship detection from commercial multispectral satellite imagery

Automatic ship detection from commercial multispectral satel...

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

作者： Daniel, Brian J. Schaum, Alan P. Allman, Eric C. Leathers, Robert A. Downes, Trijntje V. US Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819495341

Commercial multispectral satellite sensors spend much of their time over the oceans. NRL has demonstrated an automatic processing system for finding ships at sea using commercially available multispectral data. To distinguish ships from whitecaps and clouds, a water/cloud clutter subspace is estimated and a continuum fusion derived anomaly detection algorithm is applied. This provides a maritime awareness capability with an acceptable detection rate while maintaining a low rate of false alarms. The system also provides a confidence metric, which can be used to further limit the false alarm rate.

关键词： Automatic Ship Detection Continuum Fusion Maritime Domain Awareness multispectral imagery Anomaly Detection Target Detection

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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...

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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...

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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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Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery

Characterizing non-Gaussian clutter and detecting weak gaseo...

引用

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

作者： Theiler, J Foy, BR Fraser, AM Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819457914

To detect weak signals on cluttered backgrounds in high dimensional spaces (such as gaseous plumes in hyperspectral imagery) without excessive false alarms requires that the background clutter be effectively characterized. If the clutter is Gaussian, the well-known linear matched filter optimizes the sensitivity to a given plume signal while suppressing the effect of the background clutter. In practice, the background clutter is rarely Gaussian. Here we illustrate non-linear corrections to the matched filter that are optimal for two non-Gaussian clutter models and we report on parametric and nonparametric characterizations of background clutter.

关键词： imagery hyperspectral clutter non-Gaussian matched filter chemical plume

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Spectral oversampling in hyperspectral imagery

Spectral oversampling in hyperspectral imagery

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

作者： Hunt, S Sierra, H Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

This paper investigates if and how oversampling techniques can be applied in a useful manner to hyperspectral images. Oversampling occurs when the signal is sampled higher than the Nyquist frequency. If this occurs, the higher sampling rate can be traded for precision. Specifically, one bit of precision can be gained if the signal has been oversampled by a factor of four. This paper first investigates if spectral oversampling actually occurs in hyperspectral images, then looks at its usefulness in classification. Simulations were done with synthetic and real images. The results indicate that oversampling does occur for many real objects, so a knowledge of what is being searched for is crucial for knowing if oversampling techniques can be used. The classification results indicate that it takes a relatively large amount of noise for these techniques to have a significant impact on classification with synthetic images. For real images however, an improvement in classification for both supervised and unsupervised algorithms was observed for all simulations.

关键词： oversampling hyperspectral

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