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Article Dans Une Revue Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles Année : 2014

Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection

Résumé

In this article, we describe a novel unsupervised spectral image segmentation algorithm. This algorithm extends the classical Gaussian Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position. Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the number of classes. We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation. Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.
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Dates et versions

hal-01933379 , version 1 (23-11-2018)

Identifiants

Citer

Serge X. Cohen, E. Le Pennec. Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2014, 69 (2), pp.245-259. ⟨10.2516/ogst/2014013⟩. ⟨hal-01933379⟩
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