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Proceedings Paper

Endmember finding and spectral unmixing using least-angle regression
Author(s): Alexander R. Boisvert; Pierre V. Villeneuve; Alan D. Stocker
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Paper Abstract

A new endmember finder and spectral unmixing algorithm based on the LARS/Lasso method for linear regression is developed. The endmember finder is sequential; a single endmember is identified at first and further endmembers which depend on the previous ones are found. The process terminates once a pre-determined number of endmembers have been found, or when the modeling error has attained the noise floor. The unmixing algorithm is a straightforward procedure that expresses each pixel as a linear combination of endmembers in a physically meaningful way. This algorithm successfully unmixes simulated data, and shows promising results on real hyperspectral images as well.

Paper Details

Date Published: 12 May 2010
PDF: 13 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951N (12 May 2010); doi: 10.1117/12.850601
Show Author Affiliations
Alexander R. Boisvert, Space Computer Corp. (United States)
Pierre V. Villeneuve, Space Computer Corp. (United States)
Alan D. Stocker, Space Computer Corp. (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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