Spatio-spectral anomalous change detection in hyperspectral imagery

Because each pixel of a hyperspectral image contains so much information, many (successful) algorithms treat those pixels as independent samples, despite the evident spatial structure in the imagery. One way to exploit this structure is to incorporate spatial processing into pixel-wise anomalous change detection algorithms. But if this is done in the most straightforward way, a contaminated cross-covariance is produced. A spatial processing framework is proposed that avoids this contamination and enhances the performance of anomalous change detection algorithms in hyperspectral imagery.

[1]  James Theiler,et al.  Quantitative comparison of quadratic covariance-based anomalous change detectors. , 2008, Applied optics.

[2]  S. Macenka,et al.  Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1988 .

[3]  S. Rotman,et al.  Spatial-spectral filtering for the detection of point targets in multi- and hyperspectral data , 2005 .

[4]  Pramod K. Varshney,et al.  An image change detection algorithm based on Markov random field models , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  John P. Kerekes,et al.  Statistics of hyperspectral imaging data , 2001, SPIE Defense + Commercial Sensing.

[6]  James Theiler,et al.  Local Coregistration Adjustment for Anomalous Change Detection , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Joseph Meola,et al.  Airborne hyperspectral detection of small changes. , 2008, Applied optics.

[8]  Michael T. Eismann,et al.  Image misregistration effects on hyperspectral change detection , 2008, SPIE Defense + Commercial Sensing.

[9]  Alan P. Schaum,et al.  Hyperspectral change detection and supervised matched filtering based on covariance equalization , 2004, SPIE Defense + Commercial Sensing.

[10]  James Theiler,et al.  EC-GLRT: Detecting Weak Plumes in Non-Gaussian Hyperspectral Clutter Using an Elliptically-Contoured Generalized Likelihood Ratio Test , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[12]  James Theiler,et al.  Sensitivity of anomalous change detection to small misregistration errors , 2008, SPIE Defense + Commercial Sensing.

[13]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[14]  Russell C. Hardie,et al.  Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Christoph C. Borel,et al.  Improving the detectability of small spectral targets through spatial filtering , 2010, Optical Engineering + Applications.

[16]  James Theiler,et al.  Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery , 2010, IEEE Geoscience and Remote Sensing Letters.

[17]  Chris Clifton Change Detection in Overhead Imagery Using Neural Networks , 2004, Applied Intelligence.

[18]  James Theiler,et al.  Proposed Framework for Anomalous Change Detection , 2006 .

[19]  Alan A. Stocker,et al.  Advanced algorithms for autonomous hyperspectral change detection , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[20]  Neal R. Harvey,et al.  Simulation framework for spatio-spectral anomalous change detection , 2009, Defense + Commercial Sensing.

[21]  Alan D. Stocker,et al.  Automated hyperspectral target detection and change detection from an airborne platform: Progress and challenges , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[23]  James Theiler,et al.  Total least squares for anomalous change detection , 2010, Defense + Commercial Sensing.

[24]  Dimitris G. Manolakis,et al.  Using elliptically contoured distributions to model hyperspectral imaging data and generate statistically similar synthetic data , 2004, SPIE Defense + Commercial Sensing.