Progressive endmember finDing by fully constrained least squares method

Shih Yu Chen, Yen Chieh Ouyang, Chinsu Lin, Hsian Min Chen, Cheng Gao, Chein I. Chang

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

7 Citations (Scopus)

Abstract

Fully constrained least squares (FCLS) method has been widely used in linear spectral mixture analysis (LSMA). This paper presents a progressive endmember growing method by FCLS, to be called Endmember Growing FCLS (EG-FCLS). Its idea is derived from a commonly used endmember finDing algorithm, Simplex Growing Algorithm (SGA) where the criterion of finDing maximal simplex volume is replaced by least squares error unmixed by FCLS.

Original languageEnglish
Title of host publication2015 7th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467390156
DOIs
Publication statusPublished - 2015 Jul 2
Event7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 - Tokyo, Japan
Duration: 2015 Jun 22015 Jun 5

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2015-June
ISSN (Print)2158-6276

Conference

Conference7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Country/TerritoryJapan
CityTokyo
Period15-06-0215-06-05

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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