Remote Sensing Image Change Detection with Graph Interaction
Chenglong Liu
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