Sequential SAR-to-Optical Image Translation
There is a common need for optical sequence images with high spatiotemporal resolution. As a solution, Synthetic Aperture Radar (SAR)-to-optical translation tends to bring high temporal continuity of optical images and low interpretation difficulty of SAR images. Existing studies have been focused o...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2287 |
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| author | Jingbo Wei Huan Zhou Peng Ke Yaobin Ma Rongxin Tang |
| author_facet | Jingbo Wei Huan Zhou Peng Ke Yaobin Ma Rongxin Tang |
| author_sort | Jingbo Wei |
| collection | DOAJ |
| description | There is a common need for optical sequence images with high spatiotemporal resolution. As a solution, Synthetic Aperture Radar (SAR)-to-optical translation tends to bring high temporal continuity of optical images and low interpretation difficulty of SAR images. Existing studies have been focused on converting a single SAR image into a single optical image, failing to utilize the advantages of repeated observations from SAR satellites. To make full use of periodic SAR images, it is proposed to investigate the sequential SAR-to-optical translation, which represents the first effort in this topic. To achieve this, a model based on a diffusion framework has been constructed, with twelve Transformer blocks utilized to effectively capture spatial and temporal features alternatively. A variational autoencoder is employed to encode and decode images, enabling the diffusion model to learn the distribution of features within optical image sequences. A conditional branch is specifically designed for SAR sequences to facilitate feature extraction. Additionally, the capture time is encoded and embedded into the Transformers. Two sequence datasets for the sequence translation task were created, comprising Sentinel-1 Ground Range Detected data and Sentinel-2 red/green/blue data. Our method was tested on new datasets and compared with three state-of-the-art single translation methods. Quantitative and qualitative comparisons validate the effectiveness of the proposed method in maintaining radiometric and spectral consistency. |
| format | Article |
| id | doaj-art-774d90abb23d4543829821b4eccd67a4 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-774d90abb23d4543829821b4eccd67a42025-08-20T03:50:16ZengMDPI AGRemote Sensing2072-42922025-07-011713228710.3390/rs17132287Sequential SAR-to-Optical Image TranslationJingbo Wei0Huan Zhou1Peng Ke2Yaobin Ma3Rongxin Tang4School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, ChinaSchool of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Resources and Environment, Nanchang University, Nanchang 330031, ChinaInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, ChinaThere is a common need for optical sequence images with high spatiotemporal resolution. As a solution, Synthetic Aperture Radar (SAR)-to-optical translation tends to bring high temporal continuity of optical images and low interpretation difficulty of SAR images. Existing studies have been focused on converting a single SAR image into a single optical image, failing to utilize the advantages of repeated observations from SAR satellites. To make full use of periodic SAR images, it is proposed to investigate the sequential SAR-to-optical translation, which represents the first effort in this topic. To achieve this, a model based on a diffusion framework has been constructed, with twelve Transformer blocks utilized to effectively capture spatial and temporal features alternatively. A variational autoencoder is employed to encode and decode images, enabling the diffusion model to learn the distribution of features within optical image sequences. A conditional branch is specifically designed for SAR sequences to facilitate feature extraction. Additionally, the capture time is encoded and embedded into the Transformers. Two sequence datasets for the sequence translation task were created, comprising Sentinel-1 Ground Range Detected data and Sentinel-2 red/green/blue data. Our method was tested on new datasets and compared with three state-of-the-art single translation methods. Quantitative and qualitative comparisons validate the effectiveness of the proposed method in maintaining radiometric and spectral consistency.https://www.mdpi.com/2072-4292/17/13/2287SARimage translationsequence translationconditional diffusion model |
| spellingShingle | Jingbo Wei Huan Zhou Peng Ke Yaobin Ma Rongxin Tang Sequential SAR-to-Optical Image Translation Remote Sensing SAR image translation sequence translation conditional diffusion model |
| title | Sequential SAR-to-Optical Image Translation |
| title_full | Sequential SAR-to-Optical Image Translation |
| title_fullStr | Sequential SAR-to-Optical Image Translation |
| title_full_unstemmed | Sequential SAR-to-Optical Image Translation |
| title_short | Sequential SAR-to-Optical Image Translation |
| title_sort | sequential sar to optical image translation |
| topic | SAR image translation sequence translation conditional diffusion model |
| url | https://www.mdpi.com/2072-4292/17/13/2287 |
| work_keys_str_mv | AT jingbowei sequentialsartoopticalimagetranslation AT huanzhou sequentialsartoopticalimagetranslation AT pengke sequentialsartoopticalimagetranslation AT yaobinma sequentialsartoopticalimagetranslation AT rongxintang sequentialsartoopticalimagetranslation |