Low-cost updating of land-cover maps by classifiying multitemporal remote sensing images

This paper presents a novel system for automatically updating land-cover maps by classifying multitemporal remote sensing images (i.e., satellite images acquired on the same area at different times). The proposed system assumes that a training set is not available for the image to be classified (i.e., the target domain), but it is accessible for another image (i.e., the source domain) acquired on the same area at a different time. Under this assumption the proposed method aims at defining a reliable training set for the target domain on the basis of three steps. In the first step, unsupervised change detection is applied to target and source domains. Then class labels of detected unchanged training samples are transferred from the source to the target domain to initialize the training set for the target domain. In the second step, the training set is enriched by labeling new samples according to a novel active learning method. In the last step, the target domain is classified by a cascade classifier that exploits the temporal correlation between domains.