A Spatial-Temporal-Channel Attention Unet++ for High Resolution Remote Sensing Image Change Detection

Abstract

Change detection for high resolution remote sensing images is an important but challenging task. In this article, we propose a spatial-temporal-channel attention Unet++ (STC-Unet++) for remote sensing image change detection. The STC-Unet++ takes advantage of the Unet++ structure, combining semantic information to change detection. In addition, it employs a spatial-temporal-channel attention mechanism, extracting features more discriminatively and improving the change detection accuracy without increasing training time. Finally, experiments are carried out on the LEVIR-CD dataset, and the results show that the STC-Unet++ can effectively detect the changes, achieving 89.0% recall, 88.3% accuracy, 88.4% F1-score, 79.49% IoU and 94.1% AUC.