TY - GEN
T1 - Kernel-based linear spectral mixture analysis for hyperspectral image classification
AU - Liu, Keng Hao
AU - Wong, Englin
AU - Chang, Chein I.
PY - 2009
Y1 - 2009
N2 - Linear Spectral Mixture Analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.
AB - Linear Spectral Mixture Analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.
UR - http://www.scopus.com/inward/record.url?scp=72049084762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72049084762&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS.2009.5289096
DO - 10.1109/WHISPERS.2009.5289096
M3 - Conference contribution
AN - SCOPUS:72049084762
SN - 9781424446872
T3 - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
BT - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
T2 - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Y2 - 26 August 2009 through 28 August 2009
ER -