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Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images
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.
Cited literature [11 references]
https://hal.archives-ouvertes.fr/hal-01062378
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
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
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fauvel_icassp14.pdf
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