# Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation

arXiv: https://arxiv.org/abs/2012.12180 · HTML: https://mlsanity.com/paper/2012.12180/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [Accelerating Diffusion for SAR-to-Optical Image Translation via Adversarial Consistency Distillation](https://mlsanity.com/paper/2407.06095/) (2024)
- [Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline](https://mlsanity.com/paper/2203.00116/) (2022)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)
- [Deep Learning-based Cross-modal Reconstruction of Vehicle Target from Sparse 3D SAR Image](https://mlsanity.com/paper/2406.04158/) (2024)
- [Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models](https://mlsanity.com/paper/2503.19798/) (2025)
- [Deep Learning for Remote Sensing to Improve Flood Inundation Mapping](https://mlsanity.com/paper/2606.02310/) (2026)
- [HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates](https://mlsanity.com/paper/2606.18115/) (2026)

## Abstract (arXiv)

Satellite images are often contaminated by clouds. Cloud removal has received much attention due to the wide range of satellite image applications. As the clouds thicken, the process of removing the clouds becomes more challenging. In such cases, using auxiliary images such as near-infrared or synthetic aperture radar (SAR) for reconstructing is common. In this study, we attempt to solve the problem using two generative adversarial networks (GANs). The first translates SAR images into optical images, and the second removes clouds using the translated images of prior GAN. Also, we propose dilated residual inception blocks (DRIBs) instead of vanilla U-net in the generator networks and use structural similarity index measure (SSIM) in addition to the L1 Loss function. Reducing the number of downsamplings and expanding receptive fields by dilated convolutions increase the quality of output images. We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images. The restored images are evaluated with PSNR and SSIM. We compare the proposed method with state-of-the-art deep learning models and achieve more accurate results in both SAR-to-optical translation and cloud removal parts.