Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng Hao Liu, Englin Wong, Chein I. Chang

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
DOIs
出版狀態Published - 2011
事件Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII - Orlando, FL, United States
持續時間: 2011 4月 252011 4月 28

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
8048
ISSN(列印)0277-786X

Conference

ConferenceAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
國家/地區United States
城市Orlando, FL
期間11-04-2511-04-28

All Science Journal Classification (ASJC) codes

  • 電子、光磁材料
  • 凝聚態物理學
  • 電腦科學應用
  • 應用數學
  • 電氣與電子工程

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