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Proceedings Paper

Band selection for hyperspectral target detection based on a multinormal mixture anomaly detection algorithm
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Paper Abstract

The paper outlines a new method for band selection derived from a multivariate normal mixture anomaly detection method. The method consists in evaluating detection performance in terms of false alarm rates for all band configurations obtainable from an input image by selecting and combining bands according to selection criteria reflecting sensor physics. We apply the method to a set of hyperspectral images in the visible and near-infrared spectral domain spanning a range of targets, backgrounds and measurement conditions. We find optimum bands, and investigate the feasibility of defining a common band set for a range of scenarios. The results suggest that near optimal performance can be obtained using general configurations with less than 10 bands. This may have implications for the choice of sensor technology in target detection applications. The study is based on images with high spectral and spatial resolution from the HySpex hyperspectral sensor.

Paper Details

Date Published: 11 April 2008
PDF: 6 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 696606 (11 April 2008); doi: 10.1117/12.777758
Show Author Affiliations
Ingebjørg Kåsen, Norwegian Defence Research Establishment (Norway)
Anders Rødningsby, Norwegian Defence Research Establishment (Norway)
Trym Vegard Haavardsholm, Norwegian Defence Research Establishment (Norway)
Torbjørn Skauli, Norwegian Defence Research Establishment (Norway)

Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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