DOMAIN DISTRIBUTION ALIGNMENT FOR BOOSTING MULTI-MODAL REMOTE SENSING IMAGE MATCHING

Multi-modal images can obtain complementary and rich information images, which are more widely used in various applications. However, due to the different imaging mechanisms of different sensors, there are significant domain distribution differences between multi-modal images. In multi-modal image matching, existing deep learning methods should deal with the image content difference caused by rotation transformation and the domain distribution difference caused by different sensors, which are very difficult for the deep network. To address this issue, we propose to combine an instance comparison and a batch comparison to deal with image content differences and domain distribution differences, respectively. We design a new domain distribution alignment method to explicitly constrain the sample domain distribution of the multi-modal images are consistent through the domain distribution alignment loss. Extensive multi-modal remote sensing image patch matching experiments have shown the effectiveness of the proposed method. Furthermore, the proposed multi-modal domain distribution alignment method has more obvious advantages when there are significant content differences and distribution differences.