Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng-Hao Liu; Englin Wong; Chein-I Chang

doi:10.1117/12.884650

20 May 2011 Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng-Hao Liu, Englin Wong, Chein-I Chang

Author Affiliations +

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 80481P (2011) https://doi.org/10.1117/12.884650
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Keng-Hao Liu, Englin Wong, and Chein-I Chang "Kernel-based weighted abundance constrained linear spectral mixture analysis", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481P (20 May 2011); https://doi.org/10.1117/12.884650

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