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7 May 2007 Use of spectral clustering to enhance clutter suppression for hyperspectral change detection
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Hyperspectral change detection has been shown to be a promising approach for detecting subtle targets in complex backgrounds. Reported change detection methods are typically based on linear predictors that assume a space-invariant affine transformation between image pairs. Unfortunately, several physical mechanisms can lead to significant space variance in the spectral change associated with background clutter, including shadowing and other illumination variations as well as seasonal impacts on the spectral nature of vegetation, and this can lead to poor change detection performance. This paper outlines a methodology to deal with such space-variant change using spectral clustering and other related least-squares optimization techniques. Several specific algorithms are developed and applied to change imagery captured under controlled conditions, and the impacts on clutter suppression are quantified and compared. The results indicate that such techniques can provide markedly increased clutter suppression and change detection performance when the environmental conditions associated with the image pairs are substantially different.
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Michael T. Eismann and Joseph Meola "Use of spectral clustering to enhance clutter suppression for hyperspectral change detection", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651T (7 May 2007);

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