Modern remote sensing image change detection has witnessed substantial advancements by harnessing the potent feature extraction capabilities of CNNs and Transforms.Yet,prevailing change detection techniques consistently prioritize extracting semantic features related to significant alterations,overlooking the viability of directly interacting with bitemporal image features.In this letter,we propose a bitemporal image graph Interaction network for remote sensing change detection,namely BGINet-CD. More specifically,by leveraging the concept of non-local operations and mapping the features obtained from the backbone network to the graph structure space,we propose a unified self-focus mechanism for bitemporal images.This approach enhances the information coupling between the two temporal images while effectively suppressing task-irrelevant interference,Based on a streamlined backbone architecture,namely ResNet18,our model demonstrates superior performance compared to other state-of-the-art methods (SOTA) on the GZ CD dataset. Moreover,the model exhibits an enhanced trade-off between accuracy and computational efficiency,further improving its overall effectiveness

翻译：现代遥感图像变化检测通过利用CNN和Transformer强大的特征提取能力取得了显著进展。然而，现有变化检测技术始终侧重于提取与显著变化相关的语义特征，而忽略了直接交互双时相图像特征的可行性。本文提出一种用于遥感变化检测的双时相图像图交互网络，即BGINet-CD。具体而言，通过利用非局部操作的概念并将主干网络获得的特征映射到图结构空间，我们提出了一种针对双时相图像的统一自聚焦机制。该方法增强了两个时相图像之间的信息耦合，同时有效抑制了任务无关干扰。基于轻量级主干架构ResNet18，我们的模型在GZ CD数据集上展现出优于其他最先进方法（SOTA）的性能。此外，该模型在精度与计算效率之间实现了更优的权衡，进一步提升了整体有效性。