Journal of University of Chinese Academy of Sciences >
Super-resolution reconstruction of high-resolution remote sensing images for real scenes
Received date: 2023-01-30
Revised date: 2024-05-21
Online published: 2024-06-11
Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.
Jiayi ZHAO , Yong MA , Fu CHEN , Wutao YAO , Erping SHANG , Shuyan ZHANG , An LONG . Super-resolution reconstruction of high-resolution remote sensing images for real scenes[J]. Journal of University of Chinese Academy of Sciences, 2026 , 43(1) : 80 -92 . DOI: 10.7523/j.ucas.2024.054
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