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Journal of Electrical and Computer Engineering
Volume 2012 (2012), Article ID 162106, 16 pages
http://dx.doi.org/10.1155/2012/162106
Research Article

Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity

1Department CISS, Royal Military Academy, 2007 Brussels, Belgium
2Land and Air Systems Division, Norwegian Defence Research Establishment (FFI), 2007 Kjeller, Norway
3Theoretical and Applied Optics Department, French Aerospace Laboratory (ONERA), FR-31055 Toulouse Cedex 4, France
4Department of Mathematics, Royal Military Academy, Brussels, Belgium

Received 24 May 2012; Revised 22 August 2012; Accepted 9 September 2012

Academic Editor: Xiaofei Hu

Copyright © 2012 Dirk Borghys et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many anomaly detectors have been proposed in the literature. They differ in the way the background is characterized and in the method used for determining the difference between the current pixel and the background. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the background of the complete scene by a single multivariate normal probability density function. In many cases, this model is not appropriate for describing the background. For that reason a variety of other anomaly detection methods have been developed. This paper examines three classes of anomaly detectors: subspace methods, local methods, and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with diverse complexity. The results are evaluated and compared.