Endmember finding and spectral unmixing using least-angle regression

Alexander R. Boisvert; Pierre V. Villeneuve; Alan D. Stocker

doi:10.1117/12.850601

12 May 2010 Endmember finding and spectral unmixing using least-angle regression

Alexander R. Boisvert, Pierre V. Villeneuve, Alan D. Stocker

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951N (2010) https://doi.org/10.1117/12.850601
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Alexander R. Boisvert, Pierre V. Villeneuve, and Alan D. Stocker "Endmember finding and spectral unmixing using least-angle regression", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951N (12 May 2010); https://doi.org/10.1117/12.850601

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