A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

Abstract : The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.
Type de document :
Communication dans un congrès
Daoliang Li; Chunjiang Zhao. Third IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture III (CCTA), Oct 2009, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-317, pp.15-20, 2010, Computer and Computing Technologies in Agriculture III. 〈10.1007/978-3-642-12220-0_3〉
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01062132
Contributeur : Hal Ifip <>
Soumis le : mardi 9 septembre 2014 - 11:30:02
Dernière modification le : mardi 8 août 2017 - 17:16:58
Document(s) archivé(s) le : mercredi 10 décembre 2014 - 12:05:50

Fichier

0129--Jianing_Zhao.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Jianing Zhao, Wanlin Gao, Zili Liu, Guifen Mou, Lin Lu, et al.. A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm. Daoliang Li; Chunjiang Zhao. Third IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture III (CCTA), Oct 2009, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-317, pp.15-20, 2010, Computer and Computing Technologies in Agriculture III. 〈10.1007/978-3-642-12220-0_3〉. 〈hal-01062132〉

Partager

Métriques

Consultations de la notice

342

Téléchargements de fichiers

139