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Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images

Mathieu Fauvel 1 Charles Bouveyron 2 Stéphane Girard 3
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : A family of parsimonious Gaussian process models for classification is proposed in this letter. A subspace assumption is used to build these models in the kernel feature space. By constraining some parameters of the models to be common between classes, parsimony is controlled. Experimental results are given for three real hyperspectral data sets, and comparisons are done with three others classifiers. The proposed models show good results in terms of classification accuracy and processing time.
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Submitted on : Thursday, September 24, 2015 - 9:39:48 PM
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Mathieu Fauvel, Charles Bouveyron, Stéphane Girard. Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2015, 12 (12), pp.2423-2427. ⟨10.1109/lgrs.2015.2481321⟩. ⟨hal-01203269⟩



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