LAUSR

Change detection (CD) is crucial to the understanding of relationships and interactions among multitemporal high-resolution remote sensing (RS) images. However, various inherent attributes of images have different impacts on CD judgment. How to effectively use helpful information to improve the performance of CD is still a challenge. In this article, we present a boundary extraction constrained Siamese network (BESNet) to dig out the efficacy of boundary information. BESNet is a joint learning network in which a novel multiscale boundary extraction (MSBE) module is embedded. In this way, traditional and deep learning techniques are leveraged to learn together to maximize their respective strengths through cooperation. In particular, a new boundary extraction constrained (BEC) loss function combined with a contractive loss function is used to optimize the BESNet. Considering the interaction between various extracted features, a channel-shuffle fusion strategy is developed to exploit their complementary advantages between features. Our experiments show that the proposed BESNet can significantly improve the CD performance and generate more complete and clearer object boundaries. Experiments conducted on two real datasets over different scenes demonstrate its state-of-the-art performance.