Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models
文献类型：期刊论文
| 作者 | Miao, Ru3; Yang, Kai1; Zhou, Ke1; Song, Jia2; Fu, Shihao(); Liu, Cong1; Wang, Yuanxing1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:18528-18542 |
| 关键词 | Diffusion models Image reconstruction Noise Training Remote sensing Feature extraction Imaging Image restoration Degradation Data models Deep learning diffusion models generative models remote sensing super-resolution (SR) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3590687 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | In remote sensing image (RSI) super-resolution (SR), traditional deep learning methods have made remarkable progress. However, these methods struggle to handle the complex mapping between cross-sensor images. Although generative adversarial networks can reconstruct fine details, their training process is relatively challenging. Recently, diffusion-based generative models have effectively improved the visual quality of reconstructed images through the iterative reverse diffusion mechanism. Therefore, this study proposes a cross-sensor RSI SR method based on diffusion models, which aims to effectively reconstruct low-resolution (LR) RSI into high-resolution (HR) images, addressing sensor differences and information loss. To extract rich prior information from LR images and improve image reconstruction, we designed a prior feature extraction module, which aids in recovering HR features. In addition, we designed an efficient sampling strategy that starts with LR images and progressively adds noise, replacing the traditional method that begins with pure noise. This approach leverages the LR image as an approximation of the intermediate state in the Markov chain, reducing the number of steps required by the diffusion model and improving generation efficiency. Finally, we used RSI from the Sentinel-2 (10 m resolution) and Gaofen-2 (0.8 m resolution) sensors to validate the reconstruction performance of the diffusion model. Experimental results confirm that the proposed method effectively utilizes LR priors for HR reconstruction while reducing computational costs. |
| URL标识 | 查看原文 |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001547298600019 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215529]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhou, Ke |
| 作者单位 | 1.Henan Univ, Henan Engn Res Ctr Spatial Informat Proc, Henan Technol Innovat Ctr Spatio Temporal Big Data, Sch Comp & Informat Engn, Kaifeng 475004, Peoples R China; 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Henan Univ, Henan Engn Res Ctr Spatial Informat Proc, Sch Comp & Informat Engn, Kaifeng 475004, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Miao, Ru,Yang, Kai,Zhou, Ke,et al. Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:18528-18542. |
| APA | Miao, Ru.,Yang, Kai.,Zhou, Ke.,Song, Jia.,Fu, Shihao.,...&Wang, Yuanxing.(2025).Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,18528-18542. |
| MLA | Miao, Ru,et al."Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):18528-18542. |
入库方式： OAI收割
来源：地理科学与资源研究所
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