This letter 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. 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, whereas the source image samples are reweighted or possibly removed from the training set on the basis of their disagreement with the target image classification problem. This way, the consistent information available from the source image can be effectively exploited for the classification of the target image and for guiding the selection of new samples to be labeled, whereas the inconsistent information is automatically detected and removed. This approach can significantly reduce the number of new labeled samples to be collected from the target image. Experimental results on both a multispectral very high resolution and a hyperspectral data set confirm the effectiveness of the proposed method.