Hyperspectral clutter statistics, generative models, and anomaly detection

Mark Bernhardt; Jamie Heather; Oliver Watkins

doi:10.1117/12.665652

4 May 2006 Hyperspectral clutter statistics, generative models, and anomaly detection

Mark Bernhardt, Jamie Heather, Oliver Watkins

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 623321 (2006) https://doi.org/10.1117/12.665652
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Mark Bernhardt, Jamie Heather, and Oliver Watkins "Hyperspectral clutter statistics, generative models, and anomaly detection", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623321 (4 May 2006); https://doi.org/10.1117/12.665652

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available