SLDDNet: Stagewise Short and Long Distance Dependency Network for Remote Sensing Change Detection

With the rapid development of society, the pace of land change continues to accelerate. Consequently, remote sensing change detection (CD) has become a vital method for monitoring geographical information changes across various domains. However, the increasingly diverse and complex environments and structures where change targets exist pose significant challenges to CD tasks. To address these challenges, we propose a novel approach called stagewise short and long distance dependency network (SLDDNet). SLDDNet uses CNN-Transformer architecture and proposes the Transformer semantic selector to capture long-range feature dependencies and enhance semantic associations from a global perspective. Moreover, the pyramid structure feature stacking is proposed to capture short-range feature dependencies and emphasize local feature information. By integrating these two types of features at each layer, SLDDNet focuses on semantic information and improves its ability to attend to feature details. Furthermore, SLDDNet enhances target position information through axial semantic enhancement and optimizes the network training process using a deep supervision mechanism. Through extensive experiments, SLDDNet outperforms mainstream and state-of-the-art methods on three datasets. Specifically, on the LEVIR-CD dataset, SLDDNet achieves an F1 score of 91.75% and an intersection over union (IoU) of 84.76%. On the WHU-CD dataset, it achieves an F1 score of 92.76% and an IoU of 85.78%. Finally, on the GZ-CD dataset, SLDDNet achieves an F1 score of 86.61% and an IoU of 76.38%.