Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery

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

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

12 Citations (Scopus)

Abstract

This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint posterior of interest. The performance of the resulting unmixing strategy is evaluated thanks to simulations conducted on synthetic and real data.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages1009-1012
Number of pages4
ISBN (Electronic)9781457705397
ISBN (Print)9781457705380
DOIs
Publication statusPublished - 12 Jul 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing 2011
Abbreviated titleICASSP 2011
CountryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • hierarchical Bayesian analysis
  • hyperspectral images
  • MCMC methods
  • Post nonlinear mixing model

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Altmann, Y., Halimi, A., Dobigeon, N., & Tourneret, J-Y. (2011). Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 1009-1012). (IEEE International Conference on Acoustics, Speech and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2011.5946577