Semi-supervised multitemporal classification with support vector machines and genetic algorithms

This work aims at proposing a methodological solution to the challenging problem of semi-supervised classification map updating. The underlying idea of the proposed method is to update automatically the ground-truth information that will be exploited to train a support vector machine (SVM) classifier for the image under analysis. Such updating problem is formulated within a constrained multiobjective genetic algorithm (MOGA) which makes use of temporal information provided by the user under the form of allowed/forbidden class transitions. Experimental results on a multitemporal data set consisting of two multisensor (Landsat-5 TM and ERS-1 SAR) images are reported and discussed.