Axis-Based Transformer UNet for RGB Remote Sensing Image Denoising
Zhiliang Zhu, Siyi Zhang, Leiningxin Qiu, Hui Wang, Guoliang Luo · IEEE Signal Processing Letters · 2024
Remote sensing images are different from ordinary images in that they have higher resolution, contain information of a larger area, and are characterized by strip-like objects in many scenes. The traditional Transformer model based on the moving window to calculate the attention is difficult to obtain the overall features when extracting the features of strip-shaped objects and is easily interfered by the surrounding features. To address this problem, this paper innovatively designs an axial Transformer module and constructs a U-shaped hierarchical encoder-decoder structure network (ATUNet). The network improves its ability to extract global features and resist interference from irrelevant features through the axial attention mechanism. We synthesize multiple test sets with noise levels for experiments using three datasets, NWPU-RESISC45, UCMerced_LandUse, and OPTIMAL-31. The experiments show that our network has good resistance to high noise and generalization ability.