Anomaly detection based on the statistics of hyperspectral imagery

Stephen P. Catterall

doi:10.1117/12.557161

15 October 2004 Anomaly detection based on the statistics of hyperspectral imagery

Stephen P. Catterall

Proceedings Volume 5546, Imaging Spectrometry X; (2004) https://doi.org/10.1117/12.557161
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

The purpose of this paper is to introduce a new anomaly detection algorithm for application to hyperspectral imaging (HSI) data. The algorithm uses characterisations of the joint (among wavebands) probability density function (pdf) of HSI data. Traditionally, the pdf has been assumed to be multivariate Gaussian or a mixture of multivariate Gaussians. Other distributions have been considered by previous authors, in particular Elliptically Contoured Distributions (ECDs). In this paper we focus on another distribution, which has only recently been defined and studied. This distribution has a more flexible and extensive set of parameters than the multivariate Gaussian does, yet the pdf takes on a relatively simple mathematical form. The result of all this is a model for the pdf of a hyperspectral image, consisting of a mixture of these distributions. Once a model for the pdf of a hyperspectral image has been obtained, it can be incorporated into an anomaly detector. The new anomaly detector is implemented and applied to some medium wave infra-red (MWIR) hyperspectral imagery. Comparison is made with a well-known anomaly detector, and it will be seen that the results are promising.

Citation Download Citation

Stephen P. Catterall "Anomaly detection based on the statistics of hyperspectral imagery", Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); https://doi.org/10.1117/12.557161

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