Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images - Archive ouverte HAL Access content directly
Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2015

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.
Fichier principal
Vignette du fichier
grsl_fauvel_pgpda.pdf (287.36 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01203269 , version 1 (24-09-2015)

Licence

Attribution

Identifiers

Cite

Mathieu Fauvel, Charles Bouveyron, Stéphane Girard. Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images. IEEE Geoscience and Remote Sensing Letters, 2015, 12 (12), pp.2423-2427. ⟨10.1109/lgrs.2015.2481321⟩. ⟨hal-01203269⟩
1258 View
360 Download

Altmetric

Share

Gmail Facebook X LinkedIn More