Unmixing multitemporal hyperspectral images accounting for endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean Yves Toumeret, Stephen McLaughlin, Paul Honeine

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

3 Citations (Scopus)


This paper proposes an unsupervised Bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end-members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmembers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed Bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Print)9780992862633
Publication statusPublished - 2015
Event23rd European Signal Processing Conference 2015 - Nice, France
Duration: 31 Aug 20154 Sept 2015


Conference23rd European Signal Processing Conference 2015
Abbreviated titleEUSIPCO 2015


  • Bayesian algorithm
  • Hamiltonian Monte-Carlo
  • Hyperspectral unmixing
  • MCMC methods
  • spectral variability
  • temporal and spatial variability

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Unmixing multitemporal hyperspectral images accounting for endmember variability'. Together they form a unique fingerprint.

Cite this