Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery

Author

Theiler, James ; Scovel, Clint ; Wohlberg, Brendt ; Foy, Bernard R.

Author_Institution

Los Alamos Nat. Lab., Los Alamos, NM, USA

Volume

7

Issue

2

fYear

2010

fDate

4/1/2010 12:00:00 AM

Firstpage

271

Lastpage

275

Abstract

We derive a class of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically contoured (EC) distributions. These algorithms are generalizations of well-known detectors that are obtained when the EC function is Gaussian. The performance of these EC-based anomalous change detectors is assessed on real data using both real and simulated changes. In these experiments, the EC-based detectors substantially outperform their Gaussian counterparts.

Keywords

geophysical image processing; geophysical techniques; pattern recognition; Gaussian distributions; adaptive signal detection; anomalous change detection; covariance matrices; data-model ellipsoids; elliptically contoured distributions; elliptically contoured-based anomalous change detectors; hyperspectral imagery; image analysis; pattern recognition; remote sensing; Adaptive signal detection; Gaussian distributions; algorithms; covariance matrices; data-model ellipsoids; image analysis; pattern recognition; remote sensing;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2009.2032565

Filename

5299262