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.
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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 : mardi 23 mai 2017 - 11:23:53
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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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