Toward the Automatic Updating of Land-Cover Maps by a Domain-Adaptation SVM Classifier and a Circular Validation Strategy

In this paper, we address automatic updating of land-cover maps by using remote-sensing images periodically acquired over the same investigated area under the hypothesis that a reliable ground truth is not available for all the considered acquisitions. The problem is modeled in the domain-adaptation framework by introducing a novel method designed for land-cover map updating, which is based on a domain-adaptation support vector machine technique. In addition, a novel circular accuracy assessment strategy is proposed for the validation of the results obtained by domain-adaptation classifiers when no ground-truth labels for the considered image are available. Experimental results obtained on a multitemporal and multispectral data set confirmed the effectiveness and the reliability of the proposed system.