TY - JOUR
T1 - Kernel-based weighted abundance constrained linear spectral mixture analysis for remotely sensed images
AU - Liu, Keng Hao
AU - Wong, Englin
AU - Wen, Chia Hsien
AU - Chang, Chein I.
PY - 2013
Y1 - 2013
N2 - Linear spectral mixture analysis (LSMA) is a theory that can be used 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) have been developed for this purpose. Subsequently, these three techniques were further extended to Fisher's LSMA (FLSMA), weighted abundance constrained LSMA (WAC-LSMA) and kernel-based LSMA (K-LSMA). This paper combines both approaches of KLSMA and WAC-LSMA to derive a most general version of LSMA, kernel-based WACLSMA (KWAC-LSMA), which includes all the above-mentioned LSMA as its special cases. In particular, a new version of kernelizing FLSMA, referred to as kernel FLSMA (K-FLSMA) can be also developed to enhance the FLSMA performance by replacing the weighting matrix used in WAC-LSMA with a matrix specified by the within-class scatter matrix. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.
AB - Linear spectral mixture analysis (LSMA) is a theory that can be used 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) have been developed for this purpose. Subsequently, these three techniques were further extended to Fisher's LSMA (FLSMA), weighted abundance constrained LSMA (WAC-LSMA) and kernel-based LSMA (K-LSMA). This paper combines both approaches of KLSMA and WAC-LSMA to derive a most general version of LSMA, kernel-based WACLSMA (KWAC-LSMA), which includes all the above-mentioned LSMA as its special cases. In particular, a new version of kernelizing FLSMA, referred to as kernel FLSMA (K-FLSMA) can be also developed to enhance the FLSMA performance by replacing the weighting matrix used in WAC-LSMA with a matrix specified by the within-class scatter matrix. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.
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U2 - 10.1109/JSTARS.2012.2234441
DO - 10.1109/JSTARS.2012.2234441
M3 - Article
AN - SCOPUS:84877892599
SN - 1939-1404
VL - 6
SP - 531
EP - 553
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 2
M1 - 6516026
ER -