Waterfall Solutions : Publication Abstract

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Title

Hyperspectral clutter statistics, generative models and anomaly detection

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.

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Authors:

D. Dwyer, Octec Limited, UK;
M. Bernhardt, Waterfall Solutions, UK;
J. P. Heather, Waterfall Solutions, UK;
O. Watkins, Waterfall Solutions, UK;

Conference:

Defense and Security Symposium 2006. Conference 6233: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII: Modelling and Simulation