Kernel-based linear spectral mixture analysis for hyperspectral image classification

Keng Hao Liu, Englin Wong, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing
DOIs
Publication statusPublished - 2009
EventWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France
Duration: 2009 Aug 262009 Aug 28

Publication series

NameWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Conference

ConferenceWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Country/TerritoryFrance
CityGrenoble
Period09-08-2609-08-28

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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