A change-detection-driven approach to active transfer learning for classification of image time series

This paper addresses the problem of land-cover maps updating by classifying multitemporal remote sensing images (i.e., images acquired on the same area at different times) in the context of change-detection-driven active transfer learning. The proposed method is based on the assumption that training samples are available for one of the available multitemporal images (i.e., source domain), whereas they are not for the others (i.e., target domain). In order to effectively classify the target domain (i.e., update the maps obtained for the source domain according to the new information brought from another acquisition) we present a novel approach to automatically define a training set for the target domain taking advantage of its temporal correlation with the source domain. The proposed method is based on four steps. In the first step unsupervised change detection is applied to multitemporal images (i.e., target and source domains). Labels of detected unchanged training samples are propaga...