<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>3</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerroum, M A</style></author><author><style face="normal" font="default" size="100%">Hammouch, A</style></author><author><style face="normal" font="default" size="100%">Aboutajdine, D</style></author><author><style face="normal" font="default" size="100%">Bellaachia, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using the maximum Mutual Information criterion to textural Feature Selection for satellite image classification</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Communications, 2008. ISCC 2008. IEEE Symposium on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cooccurrence Matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">LDA</style></keyword><keyword><style  face="normal" font="default" size="100%">mutual information</style></keyword><keyword><style  face="normal" font="default" size="100%">PCA</style></keyword><keyword><style  face="normal" font="default" size="100%">Satellite Image Classi- fication</style></keyword><keyword><style  face="normal" font="default" size="100%">SVM</style></keyword><keyword><style  face="normal" font="default" size="100%">Textural Feature Selection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1005-1009</style></pages><isbn><style face="normal" font="default" size="100%">1530-1346 VO -</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents and evaluates the use of the maximum mutual information criterion to textural feature selection for satellite image classification. Our approach is based on a recent work of Mutual Information Feature Selector Algorithm. The effectiveness of the proposed approach is evaluated on real data. In fact, the textural features are extracted using the cooccurrence matrix from two forest zones of SPOT HRV(XS) image in the region of Rabat, Morocco. The experimental tests of this study prove that the proposed approach gives a better performance for satellite image classification than classical methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). The classifier used in this work is the support vectors machine (SVM).</style></abstract></record></records></xml>