Papers › A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical...
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images
Claas Grohnfeldt, Michael Schmitt, Xiaoxiang Zhu
In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud- and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image. Experiments on Sentinel-2 MS and Sentinel-l SAR data confirm that our extended SAR-Opt-cGAN model utilizes the auxiliary SAR information to better reconstruct MS images than an equivalent model which uses the same architecture but only single-sensor MS data as input.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cloud Removal | SEN12MS-CR | SAR-Opt-cGAN | MAE | 0.043 | #7 of 10 | Archive leaderboard | report |
| Cloud Removal | SEN12MS-CR | SAR-Opt-cGAN | PSNR | 25.59 | #7 of 10 | Archive leaderboard | report |
| Cloud Removal | SEN12MS-CR | SAR-Opt-cGAN | SAM | 15.494 | #7 of 10 | Archive leaderboard | report |
| Cloud Removal | SEN12MS-CR | SAR-Opt-cGAN | SSIM | 0.764 | #7 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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