SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS

59 citations as recorded by crossref.

1. Exploring Self-Supervised Learning for Multi-Modal Remote Sensing Pre-Training via Asymmetric Attention Fusion G. Xu et al. https://doi.org/10.3390/rs15245682
2. PLFM: Pixel-Level Merging of Intermediate Feature Maps by Disentangling and Fusing Spatial and Temporal Data for Cloud Removal A. Sebastianelli et al. https://doi.org/10.1109/TGRS.2022.3208694
3. Pix2pix Conditional Generative Adversarial Network with MLP Loss Function for Cloud Removal in a Cropland Time Series L. Christovam et al. https://doi.org/10.3390/rs14010144
4. LGE-CR: Integrating Nonlocal Gated Attention and Two-Flow Residual Dense Learning for Cloud Removal M. Li et al. https://doi.org/10.1109/LGRS.2024.3422804
5. IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-Sensing Images M. Wang et al. https://doi.org/10.1109/TGRS.2024.3378720
6. Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives Y. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.03.013
7. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion A. Meraner et al. https://doi.org/10.1016/j.isprsjprs.2020.05.013
8. Edge-SAR-Assisted Multimodal Fusion for Enhanced Cloud Removal Z. Wen et al. https://doi.org/10.1109/LGRS.2023.3307240
9. Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery P. Ebel et al. https://doi.org/10.1109/TGRS.2020.3024744
10. Self-Supervised SAR-Optical Data Fusion of Sentinel-1/-2 Images Y. Chen & L. Bruzzone https://doi.org/10.1109/TGRS.2021.3128072
11. SAR-to-optical image translation for quality enhancement Y. Luo & D. Pi https://doi.org/10.1007/s12652-021-03665-0
12. Attentive generative adversarial network for removing thin cloud from a single remote sensing image H. Chen et al. https://doi.org/10.1049/ipr2.12067
13. Reconstruction of Optical Remote Sensing Images Using Synthetic Aperture Radar Data and Diffusion Neural Networks V. Konovalov et al. https://doi.org/10.3103/S8756699025700505
14. Modality Translation in Remote Sensing Time Series X. Liu et al. https://doi.org/10.1109/TGRS.2021.3079294
15. DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal From Optical Satellite Images X. Zou et al. https://doi.org/10.1109/TGRS.2024.3365806
16. DMDiff: A Dual-Branch Multimodal Conditional Guided Diffusion Model for Cloud Removal Through SAR-Optical Data Fusion W. Zhang et al. https://doi.org/10.3390/rs17060965
17. Former-CR: A Transformer-Based Thick Cloud Removal Method with Optical and SAR Imagery S. Han et al. https://doi.org/10.3390/rs15051196
18. Robust SAR-assisted cloud removal via supervised align-guided fusion and bidirectional hybrid reconstruction A. Wang et al. https://doi.org/10.1080/17538947.2025.2472909
19. Adaptive Fusion-Based Two-Flow Spectral–Spatial Transformer Network for SAR-Based Optical Cloud Removal M. Wang et al. https://doi.org/10.1109/JSTARS.2025.3629856
20. Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network Q. Xiong et al. https://doi.org/10.3390/rs13081512
21. U-TILISE: A Sequence-to-Sequence Model for Cloud Removal in Optical Satellite Time Series C. Stucker et al. https://doi.org/10.1109/TGRS.2023.3333391
22. Attentive Contextual Attention for Cloud Removal W. Huang et al. https://doi.org/10.1109/TGRS.2024.3472645
23. Comparative Analysis of Edge Information and Polarization on SAR-to-Optical Translation Based on Conditional Generative Adversarial Networks Q. Zhang et al. https://doi.org/10.3390/rs13010128
24. Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks R. Mukherjee & D. Liu https://doi.org/10.3390/rs15235502
25. Synthesis of Multispectral Optical Images From SAR/Optical Multitemporal Data Using Conditional Generative Adversarial Networks J. Bermudez et al. https://doi.org/10.1109/LGRS.2019.2894734
26. MCGFE-CR: Cloud Removal With Multiscale Context-Guided Feature Enhancement Network Q. Bie & X. Su https://doi.org/10.1109/ACCESS.2024.3491171
27. TransFusion-CR: Two-Phase SAR-to-Optical Translation and Deep Feature Fusion for Cloud Removal R. Liu et al. https://doi.org/10.1109/TGRS.2024.3439854
28. Performance Analysis of Deep Convolutional Autoencoders with Different Patch Sizes for Change Detection from Burnt Areas P. de Bem et al. https://doi.org/10.3390/rs12162576
29. Translation-classification loss for SAR image understanding with deep learning A. Bralet et al. https://doi.org/10.1016/j.cviu.2025.104374
30. Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping N. Park et al. https://doi.org/10.3390/app13031766
31. Dehaze-TGGAN: Transformer-Guide Generative Adversarial Networks With Spatial-Spectrum Attention for Unpaired Remote Sensing Dehazing Y. Zheng et al. https://doi.org/10.1109/TGRS.2024.3435470
32. Multidiscriminator Supervision-Based Dual-Stream Interactive Network for High-Fidelity Cloud Removal on Multitemporal SAR and Optical Images Z. Wang et al. https://doi.org/10.1109/LGRS.2023.3329538
33. Thick Clouds Removing From Multitemporal Landsat Images Using Spatiotemporal Neural Networks Y. Chen et al. https://doi.org/10.1109/TGRS.2020.3043980
34. Multimodal Diffusion Bridge With Attention-Based SAR Fusion for Satellite Image Cloud Removal Y. Hu et al. https://doi.org/10.1109/TGRS.2025.3604654
35. SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks—Optimization, Opportunities and Limits M. Fuentes Reyes et al. https://doi.org/10.3390/rs11172067
36. Cloud removal with optical and SAR imagery via multimodal similarity attention K. Tu et al. https://doi.org/10.1016/j.isprsjprs.2025.05.004
37. Density-Aware Cloud Removal of Remote Sensing Imagery Using a Global–Local Fusion Transformer Q. Rui et al. https://doi.org/10.1109/TGRS.2024.3477739
38. Temporal Co-Attention Guided Conditional Generative Adversarial Network for Optical Image Synthesis Y. Weng et al. https://doi.org/10.3390/rs15071863
39. MMCANet A Multimodal and Cross-Attention Network for Cloud Removal and Exploration of Progressive Remote Sensing Images Restoration Algorithm Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3556560
40. A novel cloud removal method by fusing features from SAR and neighboring optical remote sensing images Y. Wang et al. https://doi.org/10.1371/journal.pone.0332898
41. SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions Q. Xiong et al. https://doi.org/10.3390/rs15041137
42. Cloud-Guided Fusion With SAR-to-Optical Translation for Thick Cloud Removal X. Xiang et al. https://doi.org/10.1109/TGRS.2024.3431556
43. Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images J. Gao et al. https://doi.org/10.3390/rs13193998
44. CycleGAN-Based SAR-Optical Image Fusion for Target Recognition Y. Sun et al. https://doi.org/10.3390/rs15235569
45. FCFNet: A Frequency-Domain-Guided Cross-Modal Feature Fusion Network for Cloud Removal C. Wu et al. https://doi.org/10.1109/TGRS.2026.3662395
46. Cloud Removal With SAR-Optical Data Fusion Using a Unified Spatial–Spectral Residual Network Y. Wang et al. https://doi.org/10.1109/TGRS.2023.3339210
47. Multi-Modal Convolutional Parameterisation Network for Guided Image Inverse Problems M. Czerkawski et al. https://doi.org/10.3390/jimaging10030069
48. Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS K. Zhang et al. https://doi.org/10.1016/j.ecoinf.2024.102725
49. U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal Q. Zhang et al. https://doi.org/10.1080/15481603.2024.2330185
50. SAR-to-Optical Image Translation Using Supervised Cycle-Consistent Adversarial Networks L. Wang et al. https://doi.org/10.1109/ACCESS.2019.2939649
51. SAR-to-EO image translation with multi-kernel-size convolutions J. Mu et al. https://doi.org/10.1080/01431161.2026.2633782
52. AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR Y. Wang et al. https://doi.org/10.3390/rs17020275
53. Cloud Removal in Remote Sensing Images Using Generative Adversarial Networks and SAR-to-Optical Image Translation F. Darbaghshahi et al. https://doi.org/10.1109/TGRS.2021.3131035
54. HPN-CR: Heterogeneous Parallel Network for SAR-Optical Data Fusion Cloud Removal P. Gu et al. https://doi.org/10.1109/TGRS.2025.3546489
55. Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network S. Chen et al. https://doi.org/10.3390/rs14143374
56. Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence–Sequence Model T. Peng et al. https://doi.org/10.1109/TGRS.2022.3208926
57. Multi-Stage Frequency Attention Network for Progressive Optical Remote Sensing Cloud Removal C. Wu et al. https://doi.org/10.3390/rs16152867
58. Thick Cloud Removal Under Land Cover Changes Using Multisource Satellite Imagery and a Spatiotemporal Attention Network H. Liu et al. https://doi.org/10.1109/TGRS.2023.3236106
59. SCT-CR: A synergistic convolution-transformer modeling method using SAR-optical data fusion for cloud removal J. Ma et al. https://doi.org/10.1016/j.jag.2024.103909