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The updating of classification maps, as new image acquisitions are obtained, raises the problem of ground-truth information (training samples) updating. In this context, semisupervised multitemporal classification represents an interesting though still not well consolidated approach to tackle this issue. In this letter, we propose a novel methodological solution based on this approach. Its underlying idea is to update the ground-truth information through an automatic estimation process, which exploits archived ground-truth information as well as basic indications from the user about allowed/forbidden class transitions from an acquisition date to another. This updating problem is formulated by means of the support vector machine classification approach and a constrained multiobjective optimization genetic algorithm. Experimental results on a multitemporal data set consisting of two multisensor (Landsat-5 Thematic Mapper and European Remote Sensing satellite synthetic aperture radar) images are reported and discussed.