Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images

Mathieu Fauvel 1 Charles Bouveyron 2 Stephane Girard 3
1 DYNAFOR - Dynamiques Forestières dans l'Espace Rural
2 MAP5 - UMR 8145 - Mathématiques Appliquées à Paris 5
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : A family of parsimonious Gaussian process models is presented. They allow to construct a Gaussian mixture model in a kernel feature space by assuming that the data of each class live in a specific subspace. The proposed models are used to build a kernel Markov random field (pGPMRF), which is applied to classify the pixels of a real multivariate remotely sensed image. In terms of classification accuracy, some of the proposed models perform equivalently to a SVM but they perform better than another kernel Gaussian mixture model previously defined in the literature. The pGPMRF provides the best classification accuracy thanks to the spatial regularization.
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Communication dans un congrès
ICASSP 2014 - IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2014, Florence, Italy. IEEE, pp.2913-2916, 2014, 〈10.1109/ICASSP.2014.6854133〉
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Dernière modification le : jeudi 31 mai 2018 - 09:12:02
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Mathieu Fauvel, Charles Bouveyron, Stephane Girard. Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images. ICASSP 2014 - IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2014, Florence, Italy. IEEE, pp.2913-2916, 2014, 〈10.1109/ICASSP.2014.6854133〉. 〈hal-01062378〉

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