Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images

Olivier Eches*, Jón Atli Benediktsson, Nicolas Dobigeon, Jean Yves Tourneret

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm.

Original languageEnglish
Article number6216411
Pages (from-to)5-16
Number of pages12
JournalIEEE Transactions on Image Processing
Volume22
Issue number1
DOIs
Publication statusPublished - 2013

Other keywords

  • Hyperspectral images
  • Markov random field (MRF)
  • morphological filter
  • segmentation
  • spectral unmixing

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