Nonlinear unmixing of hyperspectral images using a generalized bilinear model

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

*Corresponding author for this work

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

9 Citations (Scopus)


This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization of the accepted linear mixing model but also of a bilinear model recently introduced in the literature. Appropriate priors are chosen for its parameters in particular to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. A Metropolis-within-Gibbs algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

Original languageEnglish
Title of host publication2011 IEEE Statistical Signal Processing Workshop (SSP)
Number of pages4
ISBN (Electronic)9781457705700
Publication statusPublished - 29 Jul 2011
Event2011 IEEE Statistical Signal Processing Workshop - Nice, France
Duration: 28 Jun 201130 Jun 2011

Publication series

NameIEEE Statistical Signal Processing Workshop (SSP)
ISSN (Print)2373-0803


Conference2011 IEEE Statistical Signal Processing Workshop
Abbreviated titleSSP 2011


  • Bayesian algorithm
  • bilinear model
  • Gibbs sampler
  • Hyperspectral imagery
  • MCMC methods
  • spectral unmixing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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