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Sensitivity of anomalous change-detection to small misregistration errors

Sensitivity of anomalous change-detection to small misregist...

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

作者： Theiler, James Los Alamos Natl Lab Space & Remote Sensing Sci Los Alamos NM USA

ISBN: (纸本)9780819471574

Aroruabiv the sin-le (,reatest confound for chan-e detection al-orithms is the misre-istration of the two images in which changes are being sought. On the other hand, since the effects of misregistration are exhibited over the entire image, there is reason to hope that algorithms which are designed to deal with pervasive effects (such as illumination differences in the scene, or calibration drifts in the sensor) will be less sensitive to the inevitable misre-istration errors that occur when comparing two images. This work will describe some controlled experiments in which change detection performance is evaluated as a function of how misregistered the images (so that any are. The performance is observed to degrade quite rapidly with the amount of misregistration practical system for automated chan 'ge detection will require accurate image registration), but algorithms that are more adaptive to pervasive differences are less sensitive to the effect of misreuistration.

关键词： hyperspectral imagery image registration change detection anomaly detection

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Registration of multi-sensor remote sensing imagery by gradient-based optimization of cross-cumulative residual entropy

Registration of multi-sensor remote sensing imagery by gradi...

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

作者： Pickering, Mark R. Xiao, Yi Jia, Xiuping Univ New South Wales Univ Coll Sch Informat Technol & Elect Engn Sydney NSW 2052 Australia Australian Def Force Acad Canberra ACT Australia

ISBN: (纸本)9780819471574

For multi-sensor registration, previous techniques typically use mutual information (MI) rather than the sum-of-the-' squared difference (SSD) as the similarity measure. However, the optimization of MI is much less straightforward than is the case for SSD-based algorithms. A new technique for image registration has recently been proposed that uses an information theoretic measure called the Cross-Cumulative Residual Entropy (CCRE). In this paper we show that using CCRE for multi-sensor registration of remote sensing imagery provides an optimization strategy that converges to a global maximum with significantly less iterations than existing techniques and is much less sensitive to the initial geometric disparity between the two images to be registered.

关键词： multi-sensor image registration optimization mutual information

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Analysis of an autonomous clutter background characterization method for hyperspectral imagery

Analysis of an autonomous clutter background characterizatio...

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

作者： Romano, Joao M. Rosari, Dalton Roth, Luz Roese, Eric Willson, Paul USA Armament Res & Dev Ctr ARDEC Picatinny Arsenal NJ 07806 USA US Army Res Lab ARL Adelphi MD 20783 USA US Army Edgewood Chem & Biol Ctr ECBC Adelphi MD USA

ISBN: (纸本)9780819471574

hyperspectral ground to ground viewing perspective presents major challenges for autonomous window based detection. One of these challenges has to do with object scales uncertainty that occur when using a window-based detection approach. In a previous paper, we introduced a fully autonomous parallel approach to address the scale uncertainty problem. The proposed approach featured a compact test statistic for anomaly detection, which is based on a principle of indirect comparison;a random sampling stage, which does not require secondary information (range or size) about the targets: a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling;and a fusion of results at the end. In this paper, we demonstrate the effectiveness and robustness of this approach on different scenarios using hyperspectral imagery, where for most of these scenarios, the parameter settings were fixed. We also investigated the performance of this suite over different times of the day, where the spectral signatures of materials varied with relation to diurnal changes during the course of the day. Both visible to near infrared and longwave imagery are used in this study.

关键词： parallel anomaly detection hyperspectral imagery random sampling

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Exploration of component analysis in multi/hyperspectral image processing

Exploration of component analysis in multi/hyperspectral ima...

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

作者： Liu, Keng-Hao Chang, Chein-I Univ Maryland Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)9780819471574

Component Analysis (CA) has found many applications in remote sensing image processing. Two major component analyses are of particular interest, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) which have been widely used in signal processing. While the PCA de-correlates data samples via 2(nd) order statistics in a set of Principal Components (PCs), the ICA represents data samples via statistical independency in a set of statistically Independent Components (ICs). However, in order to for component analyses to be effective, the number of components to be generated, p must be sufficient for data analysis. Unfortunately, in multispectral imagery (MSI) p seems to be small, while p in hyperspectral imagery (HSI) seems too large. Interestingly, very little has been reported on how to deal with this issue when p is too small or too large. This paper investigates this issue. When p is too small, two approaches are developed to mitigate the problem. One is Band Expansion Process (BEP) which augments original data band dimensionality by producing additional bands via a set of nonlinear functions. The other is a kernel-based approach, referred to as Kernel-based PCA (K-PCA) which maps features in the original data space to a higher dimensional feature space via a set of nonlinear kernel. While both approaches make attempts to resolve the issue of a small p using a set of nonlinear functions, their design rationales are completely different, particularly they are not correlated. As for a large p such as HSI, a recently developed Virtual Dimensionality (VD) can be used for this purpose where the VD was originally developed to estimate number of spectrally distinct signatures. If we assume one spectrally distinct signature can be accommodated by one component, the value of p can be actually determined by the VD. Finally, experiments are conducted to explore and evaluate the utility of component analyses, specifically, PCA and ICA using BEP and K-PCA f

关键词： Band Expansion Process (BEP) hyperspectral imagery (HSI) Kernel Principal Components Analysis (K-PCA) multispectral imagery (MSI) Principal Components Analysis (PCA) Virtual Dimensionality (VD)

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High-order statistics-based approaches to endmember extraction for hyperspectral imagery

High-order statistics-based approaches to endmember extracti...

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

作者： Chu, Shih-Yu Ren, Hsuan Chang, Chein-, I Univ Baltimore Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA Natl Cent Univ Ctr Space & Remote Sensing Res Taoyuan Taiwan

ISBN: (纸本)9780819471574

Endmember extraction has received considerable interest in recent years. Many algorithms have been developed for this purpose and most of them are designed based on convexity geometry such as vertex or endpoint projection and maximization of simplex volume. This paper develops statistics-based approaches to endmember extraction in the sense that different orders of statistics are used as criteria to extract endmembers. The idea behind the proposed statistics-based endmember extraction algorithms (EEAs) is to assume that a set of endmmembers constitute the most un-correlated sample pool among all the sa me number of signatures with correlation measured by statistics. which include variance specified by 2(nd) order statistics, least squares error (LSE) also specified by 2(nd) order statistics, skewness 3(rd) order statistics, kurtosis 4(th) order statistics, k(th) moment and statistical independency specified by infinite order of statistics measured by mutual information. In order to substantiate proposed statistics-based EEAs, experiments using synthetic and real images are conducted for demonstration.

关键词： endmember extraction algorithm (EEA) high order statistics (HOS)

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McIDAS-V - A powerful data analysis and visualization tool for multi and hyperspectral environmental satellite data

McIDAS-V - A powerful data analysis and visualization tool f...

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conference on Atmospheric and Environmental Remote Sensing Data Processing and Utilization iv - Readiness for GEOSS II

作者： Achtor, Thomas Rink, Thomas Whittaker, Thomas Parker, David Santek, David Univ Wisconsin Ctr Space Sci & Engn Madison WI 53706 USA

ISBN: (纸本)9780819473059

The Mail computer Interactive Data Access System (McIDAS) project began over 30 years ago at the University of Wisconsin-Madison to analyze and visualize data from the first generation of geostationary weather satellites. McIDAS continues to provide a strong data analysis and visualization capability for the current environmental satellites. However, the next generation of operational remote sensing instruments under development for the NPOESS and GOES-R programs require software tools with expanded capability and performance to support innovative techniques for developing algorithms, visualizing data and products, and evaluating results. A project is underway at SSEC to develop the fifth generation of McIDAS, a java-based, open-source system for multispectral and hyperspectral researchers and algorithm developers that will provide powerful new data manipulation and visualization tools to work in this data rich environment. NASA EOS MODIS and AIRS data as well as MSG SEVERI and METOP IASI data are now being used in conjunction with in situ and gridded data to develop new analysis and product validation techniques in the McIDAS-V environment. This new data analysis and visualization system will support both researchers and operational users of the advanced measurement systems on NPOESS and GOES R.

关键词： McIDAS visualization data analysis satellite imagery GOES NPOESS

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Fast multi-scale regularization and segmentation of hyperspectral imagery via anisotropic diffusion and algebraic multigrid solvers

Fast multi-scale regularization and segmentation of hyperspe...

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

作者： Duarte-Carvajalino, Julio M. Sapiro, Guillermo Velez-Reyes, Miguel Castillo, Paul Univ Puerto Rico Mayaguez PR 00681 USA Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9780819466877

This paper presents an algorithm that generates a scale-space representation of hyperspectral imagery using Algebraic Multigrid (AMG) solvers. The scale-space representation is obtained by solving with AMG a vector-valued anisotropic diffusion equation, with the hyperspectral image as its initial condition. AMG also provides the necessary structure to obtain a hierarchical segmentation of the image. The scale. space representation of the hyperspectral image can be segmented in linear time complexity. Results in the paper show that improved segmentation is achieved. The proposed methodology to solve vector PDEs can be used to extend a number of techniques currently being developed for the fast computation of geometric PDEs and its application for the processing of hyperspectral and multispectral imagery.

关键词： algebraic multigrid anisotropic diffusion hyperspectral imaging iterated weighted aggregation partial differential equations remote sensing geometric scale-space

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Improved forward floating selection algorithm for chicken contaminant detection in hyperspectral imagery

Improved forward floating selection algorithm for chicken co...

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

作者： Nakariyakul, Songyot Casasent, David P. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9780819466877

Reduction of the potential health risks to consumers caused by food-borne infections is a very important food safety issue of public concern;one of the leading causes of food-borne illnesses is fecal contamination. We consider detecting fecal contaminants on chicken carcasses using hyperspectral imagery. We introduce our new improved forward floating selection (IFFS) algorithm for feature selection of the wavebands to use in hyperspectral data for classification. Our IFFS algorithm is an improvement on the state-of-the-art sequential forward floating selection (SFFS) algorithm. Our initial results indicate that our method gives an excellent detection rate and performs better than other quasi-optimal feature selection algorithms.

关键词： chicken inspection feature selection fecal contamination food safety forward floating selection (improved) hyperspectral data product inspection.

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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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The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

The impact of initialization procedures on unsupervised unmi...

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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 Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc Mayaguez CampusPOB 9042 Mayaguez PR 00681 USA

ISBN: (纸本)9780819466877

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

关键词： hyperspectral imagery spectral unmixing abundance estimation endmember determination iterative methods initialization Positive Matrix Factorization

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