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.

翻译：遥感影像中的变化检测是一项关键任务，在环境监测、城市发展和灾害管理等领域具有重要应用。变化检测涉及利用双时相图像来识别随时间发生的变化。不同时间同一位置特征之间的双时相空间关系在此过程中起着关键作用。然而，现有的变化检测网络在双时相特征提取与融合过程中往往未能充分利用这些空间关系。在本工作中，我们提出了SRC-Net：一种用于变化检测的双时相空间关系关注网络。所提出的SRC-Net包含一个感知与交互模块，该模块融入了空间关系并建立了跨分支感知机制，以增强特征提取的精度与鲁棒性。此外，我们引入了一个补丁模式联合特征融合模块，以解决现有方法中的信息丢失问题。该模块考虑了不同的变化模式并关注空间关系，从而生成更具表现力的融合特征。进一步地，我们利用这两个关系关注模块构建了一个新颖的网络，并在LEVIR-CD和WHU Building数据集上进行了实验。实验结果表明，我们的网络在保持参数量适中的同时，性能优于当前最先进的方法。我们相信，我们的方法为变化检测树立了新的范式，并将推动该领域的进一步发展。代码与模型已在https://github.com/Chnja/SRCNet公开。