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The problem of focusing on the most relevant information in a potentially overwhelming quantity of data has become increasingly important. Using irrelevant or noisy features not only can affect the accuracy of the classification results obtained but also the convergence time. In this paper several feature selection algorithms used with the Support Vector Machine (SVM) algorithm are presented. The feature selection algorithms are classified as filter and wrapper approaches. Two different wrapper techniques are presented: the first one uses the generalization error estimate of the leave-one-example-out error, while the second one uses the error estimate of the leave-one-feature-out error. Filter approaches with 4 different parameters are presented, namely: the mutual information, the FScore, and two advanced entropy measures are studied. Results in the context of change detection using satellite imagery are then discussed.