MITIGATING SPATIAL AND SPECTRAL DIFFERENCES FOR CHANGE DETECTION USING SUPER-RESOLUTION AND UNSUPERVISED LEARNING

Change detection (CD) is one of the most researched areas in remote sensing. However, most CD methods assume that the pre-change and post-change images are acquired by the same sensor, having the same set of spectral bands and same spatial resolution. This severely limits the applicability of CD methods. It is not trivial to apply the existing CD methods in multisensor scenario. Towards this direction, we propose an unsupervised CD method that can handle large differences in spatial resolution and can work with completely different set of spectral bands. The proposed method uses a self-supervised super-resolution strategy to upsample the lower resolution image, thus mitigating differences in spatial resolution. To mitigate spectral differences, a self-supervised learning strategy is used that ingests both images as input and trains a network using self-supervised loss accounting for the spectral differences in both images. Once trained this network is used in deep change vector analysis framework for change detection. We validated the proposed method in an experimental setup where the pre-change and post-change images have different spatial resolution (10 m and 20 m/pixel) and completely disjoint set of spectral bands.