This paper presents a novel application of support vector machine (SVM) based classifiers for Mars terrain image classification. SVMs are applied in conjunction with information gain ranking (IGR) that allows the induction of informative feature subsets from sample descriptions of feature vectors of a higher dimensionality. Such an integrated use of IGR and SVMs helps to enhance the effectiveness and efficiency of the classifiers, minimizing redundant and noisy features. This work is supported with comparative studies – the resultant SVM-based classifiers generally outperform MLP and KNN-based classifiers and those which use PCA-returned features.
|Number of pages||4|
|Publication status||Published - 2010|