Mask-Guided Local–Global Attentive Network for Change Detection in Remote Sensing Images

Change detection in remote sensing images is a challenging task due to object appearance diversity and the interference of complex backgrounds. Self-attention- and spatial-attention-based solutions face limitations, such as high memory consumption and an inadequate ability to capture long-range relations, leading to imprecise contextual information and restricted performance. To address these challenges, this article introduces a novel mask-guided local–global attentive network (MLA-Net). The MLA-Net incorporates a memory-efficient local–global attention module that leverages the benefits of both self-attention and spatial attention to accurately capture the local–global context. Through simultaneous exploitation of context within inter- and intrapatches and information refinement, the feature representation capability is significantly enhanced. In addition, we introduce a change mask to refine feature differences and eliminate interference from irrelevant changes caused by complex backgrounds. Accordingly, a mask loss is defined to guide the generation of the mask. Extensive experiments on the LEVIR-CD, WHU-CD, and CLCD datasets show that our MLA-Net performs better than state-of-the-art methods.