Work

Hyperspectral detection algorithms: use covariances or subspaces?

Year: 2009

Type: conference-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

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.828397

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

Institutions: MIT Lincoln Laboratory, United States Air Force Research Laboratory, United States Air Force

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2095705397

FWCI: 1.641

Cites: 12

Cited by: 37

Related to: 10