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Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images
Abstract : A family of parsimonious Gaussian process models for classification is proposed in this letter. A subspace assumption is used to build these models in the kernel feature space. By constraining some parameters of the models to be common between classes, parsimony is controlled. Experimental results are given for three real hyperspectral data sets, and comparisons are done with three others classifiers. The proposed models show good results in terms of classification accuracy and processing time.
Cited literature [23 references]
https://hal.archives-ouvertes.fr/hal-01203269
Contributor : Charles Bouveyron Connect in order to contact the contributor
Submitted on : Thursday, September 24, 2015 - 9:39:48 PM
Last modification on : Wednesday, October 27, 2021 - 2:15:49 PM
Long-term archiving on: : Tuesday, December 29, 2015 - 9:21:14 AM
Contributor : Charles Bouveyron Connect in order to contact the contributor
Submitted on : Thursday, September 24, 2015 - 9:39:48 PM
Last modification on : Wednesday, October 27, 2021 - 2:15:49 PM
Long-term archiving on: : Tuesday, December 29, 2015 - 9:21:14 AM
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