Hyperspectral detection algorithms: Use covariances or subspaces?
Name
Manolakis-2009-Hyperspectral detection algorithms Use covariances or subspaces.pdf
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1.65 MB
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Adobe PDF
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Author(s)
Manolakis, Dimitris G.
Lockwood, Ronald B.
Cooley, T.
Jacobson, J.
Date Issued
August 2009
Journal
Proceedings of SPIE--the International Society for Optical Engineering
Publisher
Society of Photo-optical Instrumentation Engineers
Citation
Manolakis, D. et al. “Hyperspectral detection algorithms: use covariances or subspaces?.” Imaging Spectrometry XIV. Ed. Sylvia S. Shen & Paul E. Lewis. San Diego, CA, USA: SPIE, 2009. 74570Q-8. © 2009 SPIE
Version
Final published version
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
MIT Department
Lincoln Laboratory
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DOI of Published Version
http://dx.doi.org/10.1117/12.828397
