Hyperspectral unmixing accounting for spatial correlations and endmember variability

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

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

Abstract

This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponDing abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each endmember. A classification map indicating the class of each pixel is also obtained based on the estimated abundances. Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of hyperspectral images.

Original languageEnglish
Title of host publication2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
PublisherIEEE
ISBN (Electronic)9781467390156
DOIs
Publication statusPublished - 23 Oct 2017

Keywords

  • endmember variability
  • Hyperspectral imagery
  • image classification
  • Markov chain Monte-Carlo

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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    Halimi, A., Dobigeon, N., Tourneret, J-Y., & Honeine, P. (2017). Hyperspectral unmixing accounting for spatial correlations and endmember variability. In 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) [8075442] IEEE. https://doi.org/10.1109/WHISPERS.2015.8075442