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A Bayesian model for joint unmixing, clustering and classification of hyperspectral data

Abstract : Supervised classification and spectral unmixing are two methods to extract information from hyperspectral images. However, despite their complementarity, they have been scarcely considered jointly. This paper presents a new hierarchical Bayesian model to perform simultaneously both analysis in order to ensure that they benefit from each other. A linear mixture model is proposed to described the pixel measurements. Then a clustering is performed to identify groups of statistically similar abundance vectors. A Markov random field (MRF) is used as prior for the corresponding cluster labels. It pro-motes a spatial regularization through a Potts-Markov potential and also includes a local potential induced by the classification. Finally, the classification exploits a set of possibly corrupted labeled data provided by the end-user. Model parameters are estimated thanks to a Markov chain Monte Carlo (MCMC) algorithm. The interest of the proposed model is illustrated on synthetic and real data.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02139416
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Submitted on : Friday, May 24, 2019 - 4:35:45 PM
Last modification on : Wednesday, September 21, 2022 - 3:28:06 PM

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  • HAL Id : hal-02139416, version 1
  • OATAO : 19746
  • PRODINRA : 424217

Citation

Adrien Lagrange, Nicolas Dobigeon, Mathieu Fauvel, Stéphane May. A Bayesian model for joint unmixing, clustering and classification of hyperspectral data. Séminaire des doctorants, Apr 2017, Toulouse, France. pp. 1-28. ⟨hal-02139416⟩

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