# Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1710.04835 · HTML: https://mlsanity.com/paper/1710.04835/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 26 of 26)

- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images](https://mlsanity.com/paper/2308.04417/) (2023)
- [GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion](https://mlsanity.com/paper/2206.02850/) (2022)
- [PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery](https://mlsanity.com/paper/2303.16565/) (2023)
- [Seeing Through Clouds in Satellite Images](https://mlsanity.com/paper/2106.08408/) (2021)
- [IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images](https://mlsanity.com/paper/2403.11870/) (2024)
- [Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space](https://mlsanity.com/paper/2503.23717/) (2025)
- [U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2305.13277/) (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)
- [AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery](https://mlsanity.com/paper/2410.23891/) (2024)
- [Attentive Contextual Attention for Cloud Removal](https://mlsanity.com/paper/2411.13042/) (2024)
- [An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images](https://mlsanity.com/paper/2209.08984/) (2022)
- [Towards Modular Machine Learning Solution Development: Benefits and Trade-offs](https://mlsanity.com/paper/2301.09753/) (2023)
- [Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal](https://mlsanity.com/paper/2504.03607/) (2025)
- [Weakly-Supervised Cloud Detection with Fixed-Point GANs](https://mlsanity.com/paper/2111.11879/) (2021)
- [SAR-to-RGB Translation with Latent Diffusion for Earth Observation](https://mlsanity.com/paper/2504.11154/) (2025)
- [SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removal](https://mlsanity.com/paper/2602.00536/) (2026)
- [CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding](https://mlsanity.com/paper/2602.12652/) (2026)
- [ATT-CR: Adaptive Triangular Transformer for Cloud Removal](https://mlsanity.com/paper/2606.05999/) (2026)
- [Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment](https://mlsanity.com/paper/2607.02471/) (2026)
- [Difference-Driven Gating: Adaptive Feature Fusion for U-Net Decoder](https://mlsanity.com/paper/2607.11096/) (2026)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)
- [MM811 Project Report: Cloud Detection and Removal in Satellite Images](https://mlsanity.com/paper/2212.11369/) (2022)
- [PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images](https://mlsanity.com/paper/2410.16955/) (2024)
- [GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation](https://mlsanity.com/paper/2608.01896/) (2026)

## Abstract (arXiv)

In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. Synthetic Aperture Radar (SAR) is such an example that improves visibility even the clouds exist. On the other hand, the spatial resolution decreases as the wavelength increases. Furthermore, the images captured by long wavelengths differs considerably from those captured by visible light in terms of their appearance. Therefore, we propose a network that can remove clouds and generate visible light images from the multispectral images taken as inputs. This is achieved by extending the input channels of cGANs to be compatible with multispectral images. The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs. In the available dataset, the proportion of images of the forest or the sea is very high, which will introduce bias in the training dataset if uniformly sampled from the original dataset. Thus, we utilize the t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of bias in the training dataset. Finally, we confirm the feasibility of the proposed network on the dataset of four bands images, which include three visible light bands and one near-infrared (NIR) band.