A Transformer-Based Network for Change Detection in Remote Sensing Using Multiscale Difference-Enhancement

Recently, transformer-based change detection methods have achieved remarkable performance by sophisticated architectures for extracting powerful feature representations. However, due to the existence of various noises in bitemporal images, there are problems such as loss of semantic objects and incompleteness that will occur in change detection. The existing transformer-based approaches do not fully address this issue. In this paper, we propose a transformer-based multiscale difference-enhancement U-shaped network and call it TUNetCD, for change detection in remote sensing. The encoder, which is composed of a multilayer Swin-Transformer block structure, can extract multilevel feature maps, further enhance these multilevel feature maps using a Swin-Transformer feature difference map processing module, and finally obtain the final change map using a lightweight decoder. We conducted comprehensive experiments on two publicly available benchmark datasets, LEVIR-CD and DSIFN-CD, to verify the effectiveness of the method, and our method outperformed other advanced transformer-based methods.