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.
Type de document :
Article dans une revue
IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2015, 12 (12), pp.2423-2427. 〈10.1109/LGRS.2015.2481321〉
https://hal.archives-ouvertes.fr/hal-01203269
Contributeur : Charles Bouveyron
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Soumis le : jeudi 24 septembre 2015 - 21:39:48
Dernière modification le : jeudi 31 mai 2018 - 09:12:02
Document(s) archivé(s) le : mardi 29 décembre 2015 - 09:21:14
Mathieu Fauvel, Charles Bouveyron, Stephane 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〉
