A post nonlinear mixing model for hyperspectral images unmixing

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

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

2 Citations (Scopus)

Abstract

This paper studies estimation algorithms for nonlinear hyperspectral image unmixing. The proposed unmixing model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. A hierarchical Bayesian algorithm and an optimization method are proposed for solving the resulting unmixing problem. The parameters involved in the proposed model satisfy constraints that are naturally included in the estimation procedure. The performance of the unmixing strategies is evaluated thanks to simulations conducted on synthetic and real data.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages1882-1885
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

Publication series

NameInternational Geoscience and Remote Sensing Symposium
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

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

Keywords

  • hyperspectral images
  • MCMC methods
  • Post nonlinear mixing model
  • Taylor approximation

ASJC Scopus subject areas

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

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