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Parsimonious Gaussian Process Models for the Classification of Multivariate 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 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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Last modification on : Wednesday, October 27, 2021 - 2:15:49 PM
Long-term archiving on: : Wednesday, December 10, 2014 - 2:15:13 PM

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Mathieu Fauvel, Charles Bouveyron, Stéphane 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. pp.2913-2916, ⟨10.1109/ICASSP.2014.6854133⟩. ⟨hal-01062378⟩

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