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Proceedings Paper

Hyperspectral detection algorithms: use covariances or subspaces?
Author(s): D. Manolakis; R. Lockwood; T. Cooley; J. Jacobson
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Paper Abstract

There are two broad classes of hyperspectral detection algorithms.1, 2 Algorithms in the first class use the spectral covariance matrix of the background clutter; in contrast, algorithms in the second class characterize the background using a subspace model. In this paper we show that, due to the nature of hyperspectral imaging data, the two families of algorithms are intimately related. The link between the two representations of the background clutter is the low-rank of the covariance matrix of natural hyperspectral backgrounds and its relation to the spectral linear mixture model. This link is developed using the method of dominant mode rejection. Finally, the effects of regularization

Paper Details

Date Published: 17 August 2009
PDF: 8 pages
Proc. SPIE 7457, Imaging Spectrometry XIV, 74570Q (17 August 2009); doi: 10.1117/12.828397
Show Author Affiliations
D. Manolakis, MIT Lincoln Lab. (United States)
R. Lockwood, Air Force Research Lab. (United States)
T. Cooley, Air Force Research Lab. (United States)
J. Jacobson, National Air and Space Intelligence Ctr. (United States)

Published in SPIE Proceedings Vol. 7457:
Imaging Spectrometry XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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