DAHT-Net: Deformable Attention-Guided Hierarchical Transformer Network Based on Remote Sensing Image Change Detection

Remote sensing image change detection (CD) refers to the automated or semi-automated detection of differences between two remote sensing images taken at different times in the same region. To achieve better global modeling and faster inference, we propose a network architecture containing a hierarchical swin transformer block and deformable attention transformers crossed for encoding and lightweight MLP decoding to solve the CD task. The deformable attention transformer allows adaptive adjustment of the relationships and weights between feature mappings to effectively combat variations and noise interference in various scenes. The alternating use of swin transformer block and deformable attention transformer ensures the efficiency as well as the flexibility of the model. The lightweight MLP approach provides better ability to extract spatial features and contextual information, as well as faster inference speed. Compared with other methods, our proposed DAHT-Net method improves F1 scores by 0.98 and 2.61 on LEVIR-CD, CDD and two publicly available benchmark datasets, respectively, and performs well on other measures. These experimental results validate that the DAHT-Net network outperforms other comparative methods and highlight its effectiveness in remote sensing image change detection. In summary, our proposed hierarchical deformable attention-guided transformer network model provides a promising solution for remote sensing image change detection with superior performance compared to other state-of-the-art methods.