Super-resolution Reconstruction of Remote Sensing Image Based on Transformer of Multi-scale Feature Fusion

To address the limitation of the existing super-resolution reconstruction of remote sensing image algorithms in fully extracting and utilizing features and coping with high computational complexity in complex scenes，a Transformer network model for super-resolution reconstruction of remote sensing image based on multi-scale feature fusion was proposed. The multi-scale residual Swin Transformer module was introduced to fully extract features and reduce the module redundancy used for flat feature extraction. A feature fusion refinement module was established that can fully extract image features to improve network performance. Based on the public UC Merced Land Use dataset，the experimental results show that the number of parameters required by the proposed model is only 61. 6% of the parameters compared with the current mainstream super-resolution reconstruction method EDSR model. The peak signal-to-noise ratio and structural similarity of the reconstruction results at different scales are increased by 0. 82 dB and 0. 024 on average compared with the EDSR model. Through comparative analysis，it is proved that the model proposed can effectively reduce the redundancy of network parameters while improving the quality of the image. It can significantly improve the quality of the reconstructed image to meet the requirements of high-resolution remote sensing image processing. © 2024 Northeast University. All rights reserved.