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Proceedings Paper

Comparative evaluation of hyperspectral anomaly detectors in different types of background
Author(s): Dirk Borghys; Ingebjorg Kåsen; Veronique Achard; Christiaan Perneel
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Paper 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 literature. They differ by the way the background is characterized and by 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 multi-variate normal distribution. 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: sub-space methods, local methods and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with different backgrounds. The results are evaluated and compared.

Paper Details

Date Published: 24 May 2012
PDF: 12 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83902J (24 May 2012); doi: 10.1117/12.920387
Show Author Affiliations
Dirk Borghys, Royal Belgian Military Academy (Belgium)
Ingebjorg Kåsen, Norwegian Defence Research Establishment (Norway)
Veronique Achard, ONERA (France)
Christiaan Perneel, Royal Belgian Military Academy (Belgium)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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