7 May 2007 Hyperspectral change detection in high clutter using elliptically contoured distributions
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Abstract
A new class of hyperspectral detection algorithm based on elliptically contoured distributions (ECDs) is described. ECDs have been studied previously, but only for modeling the tails of background clutter distributions in order better to approximate constant false alarm performance. Here ECDs are exploited to produce new target detection algorithms with performance no worse than the best prior methods. The ECD model affords two principal advantages over older methods: (1) Its selective decision surface automatically rejects outliers that are not easily modeled, and (2) it has no free parameters needing optimization. A particularly simple version of ECD has been applied to assist in automatic change detection in extreme (unnatural) clutter. The ECD version of change detection can detect low spectral contrast targets that are not easily found by standard methods, even when these use signature information. Preliminary results indicate, furthermore, that approximate forms of the component algorithms that have been implemented in deployed systems should be avoided. They can substantially degrade detection performance in high-clutter environments.
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A. Schaum, Eric Allman, John Kershenstein, Drew Alexa, "Hyperspectral change detection in high clutter using elliptically contoured distributions", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656515 (7 May 2007); doi: 10.1117/12.729632; https://doi.org/10.1117/12.729632
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