SEMANTIC DECOUPLED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CHANGE DETECTION

Self-supervised learning (SSL) has recently been introduced to remote sensing (RS) to learn in-domain transferable representations. Here, we propose a semantic decoupled representation learning for RS image change detection (CD). Typically, the object of interest (e.g., building) is relatively small compared to the vast background. Different from existing methods expressing an image into one representation vector that may be dominated by irrelevant land-covers, we disentangle representations of different semantic regions by leveraging the semantic mask. We additionally force the model to distinguish different semantic representations, which benefits the recognition of objects of interest in the downstream CD task. We construct a dataset of bitemporal images with semantic masks in an effortless manner for pre-training. Experiments on two CD datasets show our model outperforms ImageNet, indomain supervised pre-training, and several recent SSL methods.