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Proceedings Paper

Hyperspectral clutter statistics, generative models, and anomaly detection
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Paper Abstract

Detection of anomalies in hyperspectral clutter is an important task in military surveillance. Most algorithms for unsupervised anomaly detection make either explicit or implicit assumptions about hyperspectral clutter statistics: for instance that the abundance is either normally distributed or elliptically contoured. In this paper we investigate the validity of such claims. We show that while non-elliptical contouring is not necessarily a barrier to anomaly detection, it may be possible to do better. In this paper we show how various generative models which replicate the competitive behaviour of vegetation at a mathematically tractable level lead to hyperspectral clutter statistics which do not have Elliptically Contoured (EC) distributions. We develop a statistical test and a method for visualizing the degree of elliptical contouring of real data. Having observed that in common with the generative models much real data fails to be elliptically contoured, we develop a new method for anomaly detection that has good performance on non-EC data.

Paper Details

Date Published: 4 May 2006
PDF: 12 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623321 (4 May 2006); doi: 10.1117/12.665652
Show Author Affiliations
Mark Bernhardt, Waterfall Solutions Ltd. (United Kingdom)
Jamie Heather, Waterfall Solutions Ltd. (United Kingdom)
Oliver Watkins, Waterfall Solutions Ltd. (United Kingdom)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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