Joint Variation Learning of Fusion and Difference Features for Change Detection in Remote Sensing Images

Remote sensing (RS) image change detection (CD) is an Earth observation technique for detecting surface changes in the same area during a period. With the rapid development of deep learning, various deep neural networks especially Siamese ones have been widely used in the field of CD. However, they have the deficiency of insufficient contextual information aggregation, thus resulting in false and missed detections, and it is difficult to refine the detection of change edges. To alleviate these problems and obtain more accurate results, we propose an efficient self-weighted spatial-temporal attention network (SSANet). In contrast to the Siamese structure, our network is a novel joint learning framework composed of fusion subnetwork, difference subnetwork, and decoder. Fusion subnetwork is used to extract multiscale object features where we propose a multicore channel-aligning attention (MCA) module to capture the long-range semantic information for multiscale context aggregation. Difference subnetwork is used to extract the difference variation features, where we propose a feature differential reconfiguration (FDR) module to learn the temporal change information. FDR can effectively filter change information and reconstruct features to improve the perception of changed regions. To better balance the MCA and FDR modules, an asymmetric weighting (AW) module is proposed in the decoder to self-weight the multiscale features and generate the change map. Experiments demonstrate the efficiency of proposed subnetworks and modules, and the state-of-the-art performance of SSANet.