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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41-50 订阅

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Detection of unknown gas-phase chemical plumes in hyperspectral imagery

Detection of unknown gas-phase chemical plumes in hyperspect...

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

作者： Theiler, James Wohlberg, Brendt Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9780819495341

Gas-phase chemical plumes exhibit, particularly in the infrared, distinctive emission signatures as a function of wavelength. hyperspectral imagery can exploit this distinctiveness to detect specific chemicals, even at low concentrations, using matched filters that are tailored both the the specific structure of the chemical signature and to the statistics of the background clutter. But what if the chemical species is unknown? One can apply matched filters to a long list of candidate chemicals (or chemical mixtures), or one can treat the problem as one of anomaly detection. In this case, however, the anomalous signals of interest are not completely unknown. Gas spectra are generically sparse (absorbing or emitting at only a few wavelengths), and this property can be exploited to enhance the sensitivity of anomaly detection algorithms. This paper investigates the utility of sparse signal anomaly detection for the problem of finding plumes of gas with unknown chemistry in hyperspectral imagery.

关键词： hyperspectral target detection anomaly detection sparse modeling

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hyperspectral imagery transformations using real and imaginary features for improved classification

Hyperspectral imagery transformations using real and imagina...

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

作者： Castrodad, Alexey Bosch, Edward H. Resmini, Ronald Natl Geospatial Intelligence Agcy 12310 Sunrise Valley Dr Reston VA 20191 USA

ISBN: (纸本)9780819466877

Several studies have reported that the use of derived spectral features, in addition to the original hyperspectral data, can facilitate the separation of similar classes. Linear and nonlinear transformations are employed to project data into mathematical spaces with the expectation that the decision surfaces separating similar classes become well defined. Therefore, the problem of discerning similar classes in expanded space becomes more tractable. Recent work presented by one of the authors discusses a dimension expansion technique based on generating real and imaginary complex features from the original hyperspectral signatures. A complex spectral angle mapper was employed to classify the data. In this paper, we extend this method to include other approaches that generate derivative-like and wavelet-based spectral features from the original data. These methods were tested with several supervised classification methods with two hyperspectral Image (HSI) cubes.

关键词： real and imaginary transformations wavelets hyperspectral imagery classification

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Invariant subpixel target identification in hyperspectral imagery

Invariant subpixel target identification in hyperspectral im...

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conference on algorithms for multispectral and hyperspectral imagery V

作者： Thai, B Healey, G Univ Calif Irvine Comp Vis Lab Irvine CA 92697 USA

ISBN: (纸本)0819431915

We present an algorithm for subpixel material identification that is invariant to the illumination and atmospheric conditions. The target material spectral reflectance is the only prior information required by the algorithm. A target material subspace model is constructed from the reflectance using a physical model and a background subspace model is estimated directly from the image. These two subspace models are used to compute maximum likelihood estimates for the target material component and the background component at each image pixel. These estimates form the basis of a generalized likelihood ratio test for subpixel material identification. We present experimental results using HYDICE imagery that demonstrate the utility of the algorithm for subpixel material identification under varying illumination and atmospheric conditions.

关键词： subpixel detection hyperspectral imagery

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LINES OF COMMUNICATION IN LANDSAT TM imagery

LINES OF COMMUNICATION IN LANDSAT TM IMAGERY

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conference on algorithms for multispectral and hyperspectral imagery

作者： GEE, SJ HUGHES AIRCRAFT CO DEPT IMAGE EXPLOITATEL SEGUNDOCA 90245

ISBN: (纸本)0819415359

Image analysis is especially laborintensive in the multispectral domain due to the non-intuitive nature of the data. The data is frequently separated by frequency range into a set of image planes. For image analysts (lAs) and photo interpreters to derive meaningful condusions from the imagery, the data needs to be fused into a form that is coherent to the human mind. The Lines of Communication Extraction (LOCX) system was designed to merge both spectral and spatial image processing technologies into a user-friendly package that exploits the multispectral aspect of LANDSAT TM imagery without overwhelming the user with the complexities of the data. This paper discusses in detail the system components, the operations concept and user interface, and preliminary processing results. © 1994 SPIE. All rights reserved.

关键词： User interfaces

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Performance analysis of the adaptive GMRF anomaly detector for hyperspectral imagery

Performance analysis of the adaptive GMRF anomaly detector f...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Thornton, SS Moura, JMF Media Site Inc Pittsburgh PA USA

ISBN: (纸本)0819436755

We have developed an adaptive anomaly detector based on a three-dimensional Gauss-Markov random field model (GMRF) for the background clutter in hyperspectral imagery. We have shown that incorporating the modeling framework into a single-hypothesis detection scheme leads to an efficient and effective algorithm for discriminating manmade objects (the anomalies) in real hyperspectral imagery. A major feature of our GMRF anomaly detector is that it adapts to the local statistics of the clutter through the use of an approximate maximum-likelihead (approximate-ML) estimation scheme. In this paper, we focus on a thorough performance evaluation of our Adaptive GMRF Anomaly detector for hyperspectral imagery. We evaluate the detector along three directions: estimation error performance, computational cost, and detection performance. In terms of estimation error, we derive the Cramer-Rao bounds and carry out Monte Carlo simulation studies that show that the approximate-hll estimation procedure performs quite well when the fields are highly correlated, as often the case with real hyperspectral imagery. Finally we test extensively the adaptive anomaly detector with real hyperspectral imagery from both the HYDICE and SEBASS sensors. The performance of our anomaly detector compares very favorably with that of the RX-algorithm, an alternative maximum-likelihood detector used with multispectral data, while reducing by up to an order of magnitude the associated computational cost.

关键词： hyperspectral imagery anomaly detector Gauss-Markov random field approximate ML-estimation single hypothesis detection Cramer-Rao bound SEBASS HYDICE

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An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization

An algorithm for unsupervised unmixing of hyperspectral imag...

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

作者： Masalmah, YM Vélez-Reyes, M Rosario-Torres, S Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819457914

This paper presents an approach for simultaneous determination of endmembers and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF by formulating it as a nonnegative least squares problem where the cost function is expanded with a penalty term to enforce the sum to one constraint. Preliminary results using simulated and AVIRIS-Cuprite data are presented. These results show the potential of the method to solve the unsupervised unmixing problem.

关键词： hyperspectral imagery spectral unmixing positive matrix factorization

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Algorithm for Detecting Anomaly in hyperspectral imagery Using Factor Analysis

Algorithm for Detecting Anomaly in Hyperspectral Imagery Usi...

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

作者： Lo, Edisanter Ingram, John Susquehanna Univ Dept Math Sci 514 Univ Ave Selingsrove PA 17870 USA US Mil Acad West Point NY 10996 USA

ISBN: (纸本)9780819486226

hyperspectral imaging is particular useful in remote sensing to identify a small number of unknown man-made objects in a large natural background. An algorithm for detecting such anomalies in hyperspectral imagery is developed in this article. The pixel from a data cube is modeled as the sum of a linear combination of unknown random variables from the clutter subspace and a residual. Maximum likelihood estimation is used to estimate the coefficients of the linear combination and covariance matrix of the residual. The Mahalanobis distance of the residual is defined as the anomaly detector. Experimental results obtained using a hyperspectral data cube with wavelengths in the visible and near-infrared range are presented.

关键词： Anomaly detection hyperspectral imaging remote sensing

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ANALYSIS OF AOTF hyperspectral imagery

ANALYSIS OF AOTF HYPERSPECTRAL IMAGERY

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conference on algorithms for multispectral and hyperspectral imagery

作者： CHENG, LJ HAMILTON, M MAHONEY, C REYES, G JET PROP LAB PASADENACA 91109

ISBN: (纸本)0819415359

This paper reports results from an analysis of polarimetric hyperspectral imagery collected using a prototype acousto-optic tunable filter (AOTF) instrument in an outdoor environment. Spectra, derivative spectra, and polarization spectra in the image cube form were studied. The issue concerns spectral and polarization signatures of vegetation, contributions due to aerosol, and man-made object detection. The result illustrates potentials of the technology for a variety of remote sensing applications. © 1994 SPIE. All rights reserved.

关键词： Object detection

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Small-target detection in multispectral imagery with cyclic overlay processing 2

Small-target detection in multispectral imagery with cyclic ...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Schmalz, MS Hu, WC Ritter, GX UNIV FLORIDA DEPT COMP & INFORMAT SCI & ENGNGAINESVILLEFL 32611

ISBN: (纸本)0819421391

The detection of small, hostile targets in multispectral imagery is generally complicated by sensor noise, atmospheric obscurants, and spatial distortions induced by the point-spread function (PSF). Traditional methods for multispectral detection of small targets, such as signature- based discrimination predicated upon deterministic physical models, have not proven robust in the presence of camera noise at low light levels. The additional problem of target variability also confounds a signature-based approach. Due to time-dependent variations in illuminant spectra response, targets can appear to have different spectral properties at different times of day and under various weather conditions. In this paper, we discuss computationally efficient methods for locating targets that differ spectrally from their spatially adjacent backgrounds but are similar to features located elsewhere in the source image. In particular, we note that flicker effects can be produced in which target intensity appears to vary differently with background intensity. Such effects are produced computationally by cyclic overlay processing (COP), which sequentially displays monospectral band images to achieve different perceived flicker rates of target and background. When combined with knowledge about the human visual system (HVS), COP can be successfully used in conjunction with neighborhood operations to segment target regions in highly cluttered imagery. We emphasize the role of target-background contrast in potentiating flicker effects, and discuss algorithms for computing COP. Analyses emphasize computational cost and effectiveness of various COP filter configurations for detecting targets that are similar to, or partially obscured by, surrounding cover or earth. We also discuss the implementation of our algorithms on parallel processors, in particular, the parallel algebraic logic (PAC) architecture currently being implemented in cooperation with Lockheed- Martin and USAF Wright Laboratory

关键词： automated target recognition image and data compression compressive processing

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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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