A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret, Paul Honeine

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

7 Citations (Scopus)

Abstract

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image. The coefficients of this linear combination (referred to as abundances) allow the proportions of each material (endmembers) to be quantified in the image pixel. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed Bayesian algorithm exploits spatial correlations between adjacent pixels of the image and provides spectral information by achieving a spectral unmixing. It estimates both the mean and the covariance matrix of each endmember in the image. A spatial classification is also obtained based on the estimated abundances. Simulations conducted with synthetic and real data show the potential of the proposed model and the unmixing performance for the analysis of hyperspectral images.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages2469-2473
Number of pages5
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 6 Aug 2015
Event40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015

Publication series

NameInternational Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated titleICASSP 2015
CountryAustralia
CityBrisbane
Period19/04/1524/04/15

Keywords

  • endmember variability
  • Hamiltonian Monte-Carlo
  • Hyperspectral imagery
  • image classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Halimi, A., Dobigeon, N., Tourneret, J-Y., & Honeine, P. (2015). A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 2469-2473). (International Conference on Acoustics, Speech and Signal Processing (ICASSP)). IEEE. https://doi.org/10.1109/ICASSP.2015.7178415