Domain adaptation approach for classification of high resolution post-disaster data

Disaster image information mining is one of the crucial aspects in remote sensing applications. In a post disaster situation, to build learning model, new training samples are required, which are difficult to obtain. With the available pre-disaster data, the traditional algorithms cannot generalize well on the post-event situation for classification because, the data distributions are different. The proposed approach addresses this problem by domain adaptation to classify a post-disaster event by leveraging distribution changes. In this way it can augment the paucity in ground truth by using the prior information available to build the model.