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Proceedings Paper

Kernel-based weighted abundance constrained linear spectral mixture analysis
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Paper Abstract

Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained Least Squares (FCLS) for this purpose. Later on these three techniques were further extended to Fisher's LSMA (FLSMA), Weighted Abundance Constrained-LSMA (WAC-LSMA) and kernel-based LSMA (KLSMA). This paper combines both approaches of KLSMA and WACLSMA to derive a most general version of LSMA, Kernel-based WACLSMA (KWAC-LSMA) which includes all the above-mentioned LSMAs as its special cases. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.

Paper Details

Date Published: 20 May 2011
PDF: 9 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481P (20 May 2011); doi: 10.1117/12.884650
Show Author Affiliations
Keng-Hao Liu, Univ. of Maryland, Baltimore County (United States)
Englin Wong, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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