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Proceedings Paper

L1-endmembers: a robust endmember detection and spectral unmixing algorithm
Author(s): Alina Zare; Paul Gader
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Paper Abstract

A hyperspectral endmember detection and spectral unmixing algorithm based on an l1 norm factorization of the input hyperspectral data is developed and compared to a method based on l2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the l1 norm and the SPICE algorithm based on the l2 norm, simultaneously and autonomously estimate endmember spectra, abundance values and the number of endmembers needed for a hyperspectral image. The l1 norm factorization of the hyperspectral data is approximated through the use of the Huber M-estimator. Results showing the stability of the L1-Endmembers algorithm in terms of the number of endmembers estimated with noise and outliers are presented. Results indicate that the proposed algorithm is more consistent in estimating the correct number of endmembers over SPICE. However, when both algorithms determine the correct number of endmembers, SPICE results provide a better estimate of endmembers and a lower variance of endmember estimates over many runs with random initialization.

Paper Details

Date Published: 13 May 2010
PDF: 10 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951L (13 May 2010); doi: 10.1117/12.851065
Show Author Affiliations
Alina Zare, Univ. of Florida (United States)
Paul Gader, Univ. of Florida (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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