HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download

Contributor : Stephane Girard Connect in order to contact the contributor
Submitted on : Tuesday, September 9, 2014 - 4:52:37 PM
Last modification on : Wednesday, October 27, 2021 - 2:15:49 PM
Long-term archiving on: : Wednesday, December 10, 2014 - 2:15:13 PM


Files produced by the author(s)



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⟩



Record views


Files downloads