查看文章

Building change detection (BCD) plays a crucial role in urban planning and development and has received extensive attention. However, existing deep learning-based change detection methods suffer from limited accuracy, mainly due to the information loss and inadequate capability in feature extraction. To overcome these shortcomings, we propose a novel deeply supervised attention-guided network (DSA-Net) for BCD tasks in high-resolution images. In the DSA-Net, we innovatively introduce a spatial attention mechanism-guided cross-layer addition and skip-connection (CLA-Con-SAM) module to aggregate multi-level contextual information, weaken the heterogeneity between raw image features and difference features, and direct the network’s attention to changed regions. We also introduce an atrous spatial pyramid pooling (ASPP) module to extract multi-scale features. To further improve detection …