Orthogonal matching pursuit for nonlinear unmixing of hyperspectral imagery

Nareenart Raksuntorn, Qian Du, Nicolas Younan, Wei Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

A simple but effective nonlinear mixture model is adopted for nonlinear unmixing of hyperspectral imagery, where the multiplication of each pair of endmembers results in a virtual endmember, representing multiple scattering effect during pixel construction process. The analysis is followed by linear unmixing for abundance estimation. Due to a large number of nonlinear terms being added in an unknown environment, the following abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. Thus, sparse unmixing is applied to search the actual endmember set per pixel. The orthogonal matching pursuit (OMP) is adopted for this purpose. It can offer comparable results to the previously developed endmember variable linear mixture model (EVLMM) with much lower computational cost.

Original languageEnglish
Title of host publication2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-161
Number of pages5
ISBN (Electronic)9781479954032
DOIs
Publication statusPublished - 3 Sept 2014
Externally publishedYes
Event2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, China
Duration: 9 Jul 201413 Jul 2014

Publication series

Name2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

Conference

Conference2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
Country/TerritoryChina
CityXi'an
Period9/07/1413/07/14

Keywords

  • Nonlinear unmixing
  • hyperspectral imagery
  • orthogonal matching pursuit
  • sparse unmixing

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