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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81-90 订阅

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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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Urchin: An RX-derivative accounting for anisotropies in whitened clutter

Urchin: An RX-derivative accounting for anisotropies in whit...

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

作者： Daniel, Brian J. Schaum, Alan P. USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819481597

The most widespread methods of anomaly detection in hyperspectral imagery (HSI) are the RX algorithm and its variants (e.g. Subspace RX). RX is optimal for any unimodal elliptically contoured distribution (ECD), and in certain data sets, it misinterprets any deviations from this model as true anomalies. Singleton outliers are by definition anomalous, but other RX detections can arise from less severe departures from the ECD, in the form of spectral "prominences." We describe a method that mitigates such persistent false alarms by augmenting RX in a recursive process with truncated versions of the Adaptive Cosine Estimator (ACE). ACE is applied to RX exceedances that arise from prominences, bulges appearing in the whitened clutter distribution that indicate anisotropy. The ACE-augmented RX decision surface resembles a sea urchin.

关键词： Anomaly detection hyperspectral imagery RX algorithm target detection

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Analysis of spectral data using spatial context 25

Analysis of spectral data using spatial context

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Metcalf, Jeremy P. Olsen, Richard C. Naval Postgrad Sch 833 Dyer Rd Monterey CA 93943 USA

ISBN: (纸本)9781510626386

Spectral LiDAR analysis can be enabled by the use of spatial context, spatial structure, and prior information in the form of map data. LiDAR intensity imagery are analyzed here using an object-based approach which segments the data according to vector information obtained from OpenStreetMap and other vector map information. Polygons and features from the map vectors are used to establish regions of interest for analysis. This automates the training process for use of traditional statistical classifiers and machine learning algorithms. Map-derived objects can demonstrate multiple spectral components which must be resolved to define the primary object, and its semantic label.

关键词： multispectral imagery image segmentation feature extraction Optech Titan GEOBIA LiDAR intensity

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Anomaly detection of stationary targets in pan-sharpened IKONOS multis pectral imagery

Anomaly detection of stationary targets in pan-sharpened IKO...

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

作者： Meng, J Marble, AE Zhang, Y Secker, J Univ New Brunswick Fredericton NB E3B 5A3 Canada

ISBN: (纸本)0819457914

multispectral (MS) and hyperspectral (HS) sensors can facilitate target or anomaly detection in clutter since natural clutter and man-made objects differ in the energy they radiate across the electromagnetic spectrum. Previous research in anomaly detection has formulated two popular algorithms: those based on Gauss-Markov Random Fields (GMRF) and the so-called RX-detector. Performance of these algorithms is dependent on a number of issues including spatial resolution, spectral correlation between the imaging bands, clutter/target model accuracy and the acquired data's signal-to-noise ratio (SNR). This paper provides a comparison study of the anomaly detection performance of the RX-detector and the GMRF-based algorithm using: (1) 4m MS imagery acquired from the IKONOS satellite and (2) pansharpened 1m MS imagery created by fusing the 4m MS and the associated 1m panchromatic image sets. The study will be based on the detection performance for stationary and slow moving targets selected from imagery acquired during training exercises at Canadian Forces Base (CFB) Petawawa and CFB Wainwright, Canada.

关键词： Sensors Target detection Detection and tracking algorithms Earth observing sensors High resolution satellite images Spatial resolution Roads Distance measurement Satellite imaging Satellites

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SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of hyperspectral imagery 21

SLIC Superpixels for Efficient Graph-Based Dimensionality Re...

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

作者： Zhang, Xuewen Chew, Selene E. Xu, Zhenlin Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Ctr Appl & Computat Math Rochester NY 14623 USA

ISBN: (纸本)9781628415889

Nonlinear graph-based dimensionality reduction algorithms such as Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) have been shown to be very effective at yielding low-dimensional representations of hyperspectral image data. However, the steps of graph construction and eigenvector computation required by LE and SE can be prohibitively costly as the number of image pixels grows. In this paper, we propose pre-clustering the hyperspectral image into Simple Linear Iterative Clustering (SLIC) superpixels and then performing LE- or SE-based dimensionality reduction with the superpixels as input. We then investigate how different superpixel size and regularity choices yield trade-offs between improvements in computational efficiency and accuracy of subsequent classification using the low-dimensional representations.

关键词： Superpixels Dimensionality Reduction Laplacian Eigenmaps Schroedinger Eigenmaps Classification

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Regularization for spectral matched filter and RX anomaly detector

Regularization for spectral matched filter and RX anomaly de...

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

作者： Nasrabadi, Nasser M. Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819471574

This paper describes a new adaptive spectral matched filter and a modified RX-based anomaly detector that incorporates the idea of regularization (shrinkage). The regularization has the effect of restricting the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called regulanization coefficient. In this paper the sum-of-squares of the filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also provided. Experimental results for detecting and recognizing targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters and RX algorithm.

关键词： regularized spectral matched filter spectral matched filter RX algorithm regularized RX algorithm regularization shrinkage hyperspectral imagery automatic target recognition

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Using Worldview-2 Vis-NIR multispectral imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios

Using Worldview-2 Vis-NIR Multispectral Imagery to Support L...

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

作者： Wolf, Antonio F. Ball Aerosp & Technol Corp Fairborn OH 45324 USA

ISBN: (纸本)9780819490681

multispectral imagery (MSI) provides information to support decision making across a growing number of private and industrial applications. Among them, land mapping, terrain classification and feature extraction rank highly in the interest of those who analyze the data to produce information, reports, and intelligence products. The 8 nominal band centers of WorldView-2 allow us to use non-traditional means of measuring the differences which exist in the features, artifacts, and surface materials in the data, and we can determine the most effective method for processing this information by exploiting the unique response values within those wavelength channels. The difference in responses across select bands can be sought using normalized difference index ratios to measure moisture content, indicate vegetation health, and distinguish natural features from man-made objects. The focus of this effort is to develop an approach to measure, identify and threshold these differences in order to establish an effective land mapping and feature extraction process germane to WorldView-2 imagery.

关键词： multispectral feature extraction land use index ratio Worldview-2 commercial imagery

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Restoration, target recognition, and countermeasure removal using the Computed Tomographic Imaging Spectrometer (CTIS)

Restoration, target recognition, and countermeasure removal ...

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

作者： Schau, HC Soulon, RE Hall, GL Garrood, DJ Raytheon Electronic Systems (United States)

Computed Tomographic Imaging Spectrometers are described and shown to be capable of providing real-time flash multispectral imagery. Restoration equations and techniques for optimizing performance are described. Exper... 详细信息

ISBN: (纸本)0819444758

关键词： imaging spectrometer multispectral imagery diffractive optical elements

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hyperspectral-multispectral image fusion using nearest-neighbor diffusion-based sharpening algorithm 28

Hyperspectral-multispectral image fusion using nearest-neigh...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXViiI

作者： Ducay, Rey Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510650657;9781510650640

The nearest-neighbor diffusion-based algorithm (NNDiffuse) has seen great success in multispectral pansharpening. Here, we extend the capabilities of NNDiffuse to perform image fusion of high-res multispectral and low-res hyperspectral images (HRMSI+LRHSI fusion). Unlike learning-based frameworks which are computationally expensive and require extensive optimization and/or training time, NNDiffuse is fast, radiometrically accurate (introduces less spectral distortions compared to other state-of-the-art methods), and requires no training data. We introduce the utility of NNDiffuse in hyperspectral-panchromatic and hyperspectral-multispectral sharpening and look at workflows dealing with low-res HSI bands that fall outside the HRMSI spectral response functions. Sharpened image quality is assessed using image-wide metrics. Sharpening performance is also measured on the fused images in terms of their utility in pixel classification and ACE target-background separability. NNDiffuse and DL- and non DL-based methods are presented using hyperspectral imagery from SHARE2012, AVIRIS-NextGen, Hyperspec-VNIR-C, and ROSIS sensors.

关键词： hyperspectral imagery panchromatic sharpening ACE target detection

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Anomaly detection in hyperspectral imagery: statistics- vs. graph-based algorithms 22

Anomaly detection in hyperspectral imagery: statistics- vs. ...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Berkson, Emily E. Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510600812

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution;however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

关键词： anomaly detection topological anomaly detector (TAD) Reed-Xiaoli (RX) subspace-RX (SSRX) statistical graph-based scene clutter

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