Bilateral Semantic Fusion Siamese Network for Change Detection from Multitemporal Optical Remote Sensing Imagery

Change detection (CD) is an essential task in optical remote sensing, and it can be used to extract the valid information from sequential multitemporal images. However, since the character of long-term revisiting and very high resolution (VHR) development, the great differences of illumination, season, and interior textures between bitemporal images bring considerable challenges for pixel-wise CD. In this letter, focusing on accurate pixel-wise CD, a bilateral semantic fusion Siamese network (BSFNet) is proposed. First, to better map bitemporal images into semantic feature domain for comparison, a novel BSFNet is designed to effectively integrate shallow and deep semantic features, which can provide pixel-wise CD results with complete regions and clear boundary locations. Then, in order to facilitate the reasonable convergence of the proposed BSFNet, a scale-invariant sample balance (SISB) loss is designed for metric learning to avoid the problems of sample imbalance and scale variance. Finally, extensive experiments are carried out on two published CDD and LEVIR CD datasets, and results indicate that the proposed BSFNet can provide superior performance than the other state-of-the-art methods. Our work is available at https://github.com/ClarissaDHL/BSFNet.