SRC-Net: Bitemporal Spatial Relationship Concerned Network for Change Detection

Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bitemporal images to identify changes over time. The bitemporal spatial relationships between features at the same location at different times play a key role in this process. However, existing change detection networks often do not fully leverage these spatial relationships during bitemporal feature extraction and fusion. In this work, we propose SRC-Net: a bitemporal spatial relationship concerned network for CD. The proposed SRC-Net includes a perception and interaction module that incorporates spatial relationships and establishes a cross-branch perception mechanism to enhance the precision and robustness of feature extraction. In addition, a patch–mode joint feature fusion module is introduced to address information loss in current methods. It considers different change modes and concerns about spatial relationships, resulting in more expressive fusion features. Furthermore, we construct a novel network using these two relationship concerned modules and conducted experiments on the LEVIR-CD and WHU Building datasets. The experimental results demonstrate that our network outperforms state-of-the-art methods while maintaining a modest parameter count. We believe that our approach sets a new paradigm for CD and will inspire further advancements in the field.