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Proceedings Paper

Hyperspectral change detection in high clutter using elliptically contoured distributions
Author(s): A. Schaum; Eric Allman; John Kershenstein; Drew Alexa
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Paper 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.

Paper Details

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656515 (7 May 2007); doi: 10.1117/12.729632
Show Author Affiliations
A. Schaum, Naval Research Lab. (United States)
Eric Allman, Naval Research Lab. (United States)
John Kershenstein, Naval Research Lab. (United States)
Drew Alexa, U.S. Civil Air Patrol (United States)

Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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