  Change detection (CD) in remote sensing imagery is a crucial task with
applications in environmental monitoring, urban development, and disaster
management. CD involves utilizing bi-temporal images to identify changes over
time. The bi-temporal 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 bi-temporal feature extraction and fusion. In this work,
we propose SRC-Net: a bi-temporal 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.
Additionally, 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 (SOTA) methods while maintaining a modest
parameter count. We believe our approach sets a new paradigm for change
detection and will inspire further advancements in the field. The code and
models are publicly available at https://github.com/Chnja/SRCNet.
