TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection

Abstract

In the field of remote sensing change detection, accurately capturing temporal change information and efficiently integrating multilevel information is a major challenge. In order to extend the sensory domain and optimize the information fusion, the model is able to capture temporal-spatial change features more accurately and improve the accuracy of change detection. In this article, we propose a temporal-spatial multiscale graph attention network (TSMGA), specifically, TSMGA employs a pair of pretrained ResNet18 for effective multiscale feature extraction, and in order to enhance the disparity information of the bitemporal images, we also design the temporal fusion block to emphasize the changed areas. The spatial and channel features of multiscale disparity features are enhanced by multiscale spatial-channel aggregation module. To enable more robust and efficient exploration of more global contextual information, we are inspired to introduce shortest path graph attention, which allows for a more informative and complete exploration of the global context, and furthermore allows for more efficient gathering information from far-off neighbors to the central node. In order to ensure comprehensive utilization of local and global features and to significantly improve the clarity and detail retention of the output image, global context residual fusion module (GCRFM) is designed, GCRFM efficiently utilizes the complementary information of the feature maps for fusing and recovering spatial details and variation information. We validate the effectiveness and advancement of TSMGA on three classical datasets (LEVIR-CD, WHU-CD, GZ-CD), and the experimental results show that TSMGA achieves the state-of-the-art performance level.