Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models
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 proce...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11085125/ |
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| author | Ru Miao Kai Yang Ke Zhou Jia Song Shihao Fu Cong Liu Yuanxing Wang |
| author_facet | Ru Miao Kai Yang Ke Zhou Jia Song Shihao Fu Cong Liu Yuanxing Wang |
| author_sort | Ru Miao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d8c91feb509045aa9a96d361675ffc82 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d8c91feb509045aa9a96d361675ffc822025-08-20T03:41:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118185281854210.1109/JSTARS.2025.359068711085125Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion ModelsRu Miao0https://orcid.org/0000-0003-0349-2971Kai Yang1https://orcid.org/0009-0008-9656-8392Ke Zhou2https://orcid.org/0009-0006-6739-4448Jia Song3https://orcid.org/0000-0002-9051-1925Shihao Fu4https://orcid.org/0009-0003-4194-7997Cong Liu5Yuanxing Wang6School of Computer and Information Engineering, Henan Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan Engineering Research Center of Spatial Information Processing, Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan Engineering Research Center of Spatial Information Processing, Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Kaifeng, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaSchool of Computer and Information Engineering, Henan Engineering Research Center of Spatial Information Processing, Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan Engineering Research Center of Spatial Information Processing, Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Kaifeng, ChinaIn 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.https://ieeexplore.ieee.org/document/11085125/Deep learningdiffusion modelsgenerative modelsremote sensingsuper-resolution (SR) |
| spellingShingle | Ru Miao Kai Yang Ke Zhou Jia Song Shihao Fu Cong Liu Yuanxing Wang 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 Deep learning diffusion models generative models remote sensing super-resolution (SR) |
| title | Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models |
| title_full | Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models |
| title_fullStr | Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models |
| title_full_unstemmed | Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models |
| title_short | Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models |
| title_sort | research on cross sensor remote sensing image super resolution method based on diffusion models |
| topic | Deep learning diffusion models generative models remote sensing super-resolution (SR) |
| url | https://ieeexplore.ieee.org/document/11085125/ |
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