Unmixing hyperspectral images using the generalized bilinear model

Abderrahim Halimi, Yoann Altmann, Nicolas Dobigeon, Jean-Yves Tourneret

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

38 Citations (Scopus)

Abstract

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages1886-1889
Number of pages4
ISBN (Electronic)9781457710056
ISBN (Print)9781457710032
DOIs
Publication statusPublished - 20 Oct 2011
Event2011 IEEE International Geoscience and Remote Sensing Symposium - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS 2011
CountryCanada
CityVancouver, BC
Period24/07/1129/07/11

Keywords

  • Bayesian inference
  • bilinear model
  • gradient descent algorithm
  • hyperspectral imagery
  • least square algorithm
  • MCMC methods
  • spectral unmixing

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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