Hyperspectral detection algorithms: use covariances or subspaces?

D. Manolakis; R. Lockwood; T. Cooley; J. Jacobson

doi:10.1117/12.828397

17 August 2009 Hyperspectral detection algorithms: use covariances or subspaces?

D. Manolakis, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 7457, Imaging Spectrometry XIV; 74570Q (2009) https://doi.org/10.1117/12.828397
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

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

Citation Download Citation

D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson "Hyperspectral detection algorithms: use covariances or subspaces?", Proc. SPIE 7457, Imaging Spectrometry XIV, 74570Q (17 August 2009); https://doi.org/10.1117/12.828397

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