Semi-supervised remote sensing image classification with cluster kernels

Détails

ID Serval
serval:BIB_94F70DCA4B66
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Semi-supervised remote sensing image classification with cluster kernels
Périodique
IEEE Geoscience and Remote Sensing Letters
Auteur(s)
Tuia D., Camps-Valls G.
ISSN
1545-598X
Statut éditorial
Publié
Date de publication
04/2009
Peer-reviewed
Oui
Volume
6
Numéro
1
Pages
224-228
Langue
anglais
Résumé
A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.
Mots-clé
Bagged and cluster kernels, image classification, kernel methods, support vector (SV) machine (SVM)
Web of science
Création de la notice
04/02/2009 18:19
Dernière modification de la notice
03/03/2018 19:38
Données d'usage