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In this paper, a novel method for semisupervised classification with limited training samples is presented. Its aim is to exploit unlabeled data available at zero cost in the image under analysis for improving the accuracy of a classification process based on support vector machines (SVMs). It is based on the idea to augment the original set of training samples with a set of unlabeled samples after estimating their label. The label estimation process is performed within a multiobjective genetic optimization framework where each chromosome of the evolving population encodes the label estimates as well as the SVM classifier parameters for tackling the model selection issue. Such a process is guided by the joint minimization of two different criteria which express the generalization capability of the SVM classifier. The two explored criteria are an empirical risk measure and an indicator of the classification model sparseness, respectively. The experimental results obtained on two multisource remote sensing data sets confirm the promising capabilities of the proposed approach, which allows the following: (1) taking a clear advantage in terms of classification accuracy from unlabeled samples used for inflating the original training set and (2) solving automatically the tricky model selection issue.