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

提出了一种基于图交互网络的双时相遥感变化检测方法，并采用 ResNet18 作为骨干网络，在 GZ CD 数据集上取得了优异的性能。

基于图交互的遥感图像变化检测

Remote Sensing Image Change Detection with Graph Interaction

Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local operation computes the response at a position as a
weighted sum of the features at all positions. This building block can be
plugged into many computer vision architectures. On the task of video
classification, even without any bells and whistles, our non-local models can
compete or outperform current competition winners on both Kinetics and Charades
datasets. In static image recognition, our non-local models improve object
detection/segmentation and pose estimation on the COCO suite of tasks. Code is
available at this https URL .

该论文提出了非局部操作作为一种用于捕捉长程依赖性的通用组建块，可以在许多计算机视觉体系结构中使用，并且在视频分类和静态图像识别中表现优异。