Endmember finding and spectral unmixing using least-angle regression

Autor:	Alexander R. Boisvert, Pierre V. Villeneuve, Alan D. Stocker
Rok vydání:	2010
Předmět:	Endmember Pixel business.industry Least-angle regression Hyperspectral imaging Pattern recognition Regression analysis Lasso (statistics) Linear regression Computer vision Artificial intelligence business Linear combination Mathematics
Zdroj:	SPIE Proceedings.
ISSN:	0277-786X
DOI:	10.1117/12.850601
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3f22015e33561e661e70ba7f8af7bff5 https://doi.org/10.1117/12.850601 Zobrazit plný text záznamu