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Proceedings Paper

A hyperspectral anomaly detector based on partialing out a clutter subspace
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Paper Abstract

An anomaly detector for hyperspectral imaging based on partialling out the effect of the clutter subspace is devised. The partialling maximizes the squared correlation between each spectral component and a linear predictor, with no restrictions on the form of the probability distribution. The detection step is defined by thresholding a Mahalanobis measure of the prediction error. The method is compared to conventional anomaly detectors using VNIR hyperspectral imagery.

Paper Details

Date Published: 27 April 2009
PDF: 4 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733404 (27 April 2009); doi: 10.1117/12.821012
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)
Alan Schaum, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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