INTERACTIVE DOMAIN ADAPTATION TECHNIQUE FOR THE CLASSIFICATION OF REMOTE SENSING IMAGES

This paper presents a novel interactive domain-adaptation technique based on active learning for the classification of remote sensing (RS) images. The proposed method aims at adapting the supervised classifier trained on a given RS source image to make it suitable for classifying a different but related target image. The two images can be acquired in different locations and/or at different times, but present the same set of land-cover classes. The proposed approach iteratively selects the most informative samples of the target image to be labeled by the user and included in the training set, while the source-image samples are re-weighted or possibly removed from the training set on the basis of their disagreement with the target image classification problem. In this way, the consistent information available from the source image can be effectively exploited for the classification of a target image and for guiding the user in the selection of the new samples to be labeled, whereas the inconsistent information is automatically detected and removed. Experimental results on a Very High Resolution (VHR) multispectral dataset confirm the effectiveness of the proposed method.