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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71-80 订阅

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Performance comparison of hyperspectral target detection algorithms in altitude varying scenes

Performance comparison of hyperspectral target detection alg...

引用

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

作者： Cisz, AP Schott, JR Rochester Inst Technol Digital Imaging & Remote Sensing Lab Rochester NY 14623 USA

ISBN: (纸本)0819457914

Many different hyperspectral target detection algorithms have been developed and tested under various assumptions, methods, and data sets. This work examines the spectral angle mapper (SAM), adaptive coherence estimator (ACE), and constrained energy maximization (CEM) algorithms. Algorithm performance is examined over multiple images, targets, and backgrounds. Methods to examine algorithm performance are plentiful and several different metrics are used here. Quantitative metrics are used to make direct comparisons between algorithms. Further analysis using visual performance metrics is made to examine interesting trends in the data. Results show an increase in detection algorithm performance as image altitude increases and spatial information decreases. Theories to explain this phenomenon are introduced.

关键词： target detection matched filters hyperspectral imagery performance metrics

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Simulation of MSI imagery from HSI data

Simulation of MSI imagery from HSI data

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

作者： Anderson, DS Keaffaber, B Wasky, R Veridian Engineering (United States)

ISBN: (纸本)0819436755

In this paper, a process of converting hyperspectral (HSI) data to multispectral (MSI) data is described. Accomplishing this conversion involves several steps: (1) data-to-signal conversion using the modulation transfer function (MTF) and the Spectral Response of the HSI sensor, (2) removing atmospheric transmittance losses from the HSI data using the MODTRAN code, (3) reintroducing atmospheric transmittance effects for each HSI band over selected MSI slant ranges, (4) grouping appropriate HSI bands into the broader MSI bands, (5) applying a spatial sampling algorithm to simulate different MSI ground sample distances (GSDs), and (6) performing signal-to-data conversion using the MTF and Spectral Response of the MSI sensor. Results are presented for two different slant ranges (GSD levels), each with three meteorological ranges.

关键词： Sensors multispectral imaging Modulation transfer functions Atmospheric corrections Image processing Transmittance Atmospheric modeling Atmospheric sensing Data conversion Image sensors

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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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Spatial-Spectral Dimensionality Reduction of hyperspectral imagery with Partial Knowledge of Class Labels 21

Spatial-Spectral Dimensionality Reduction of Hyperspectral I...

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

作者： Cahill, Nathan D. Chew, Selene E. Wenger, Paul S. Rochester Inst Technol Sch Math Sci Ctr Appl & Computat Math Rochester NY 14623 USA

ISBN: (纸本)9781628415889

Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) are effective dimensionality reduction algorithms that are capable of integrating both the spatial and spectral information inherent in a hyperspectral image. In this paper, we consider how to extend LE- and SE-based spatial-spectral dimensionality reduction algorithms to situations where partial knowledge of class labels exists, for example, when a subset of pixels has been manually labeled by an expert user. This partial knowledge is incorporated through the use of cluster potentials, turning each underlying algorithm into an instance of SE. Using publicly available data, we show that incorporating this partial knowledge improves the performance of subsequent classification algorithms.

关键词： Dimensionality reduction Laplacian eigenmaps Schroedinger eigenmaps spatial-spectral fusion

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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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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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Normal compositional models: Generalizations and applications

Normal compositional models: Generalizations and application...

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

作者： Stein, DWJ MIT Lincoln Lab Grp 97 Lexington MA 02420 USA

ISBN: (纸本)0819444758

The normal compositional model (NCM) is a descriptive model that explicitly accounts for sub-pixel mixing and random variation of the spectrum of a material. In this paper the normal compositional model, defined in an earlier work, is extended to include an additive term that may represent path radiance and additive sensor noise. If the covariance matrix of the additive term is non-singular, as may be assumed since it includes the covariance matrix of the additive noise, the covariance matrix of the other classes need not be non-singular. Thus the current model synthesizes the linear unmixing and Gaussian clustering algorithms. Anomaly and matched target detection algorithms based on these three models are compared using ocean hyperspectral imagery, and for these data the NCM approach reduces the false alarm probability by more than an order of magnitude. The linear mixture and normal compositional models separate surface reflections and upwelling light more effectively than the Gaussian clustering algorithm. Furthermore, greater inter-band correlation is estimated using the subpixel covariance estimation methodology than using the pure pixel modeling approach.

关键词： hyperspectral imagery sub-pixel analysis classification linear unmixing detection segmentation

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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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