# Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

arXiv: https://arxiv.org/abs/2009.13015 · HTML: https://mlsanity.com/paper/2009.13015/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [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)
- [Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2401.15105/) (2024)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (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)
- [Memory augment is All You Need for image restoration](https://mlsanity.com/paper/2309.01377/) (2023)
- [SpA-Former: Transformer image shadow detection and removal via spatial attention](https://mlsanity.com/paper/2206.10910/) (2022)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [SAM-helps-Shadow:When Segment Anything Model meet shadow removal](https://mlsanity.com/paper/2306.06113/) (2023)
- [Coarse-to-fine Task-driven Inpainting for Geoscience Images](https://mlsanity.com/paper/2211.11059/) (2022)
- [Attentive Contextual Attention for Cloud Removal](https://mlsanity.com/paper/2411.13042/) (2024)
- [DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing](https://mlsanity.com/paper/2504.14785/) (2025)
- [Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal](https://mlsanity.com/paper/2501.05265/) (2025)
- [Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal](https://mlsanity.com/paper/2504.03607/) (2025)
- [Task-Guided Prompting for Unified Remote Sensing Image Restoration](https://mlsanity.com/paper/2604.02742/) (2026)
- [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)
- [ATT-CR: Adaptive Triangular Transformer for Cloud Removal](https://mlsanity.com/paper/2606.05999/) (2026)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)
- [Cloud Removal from Satellite Images](https://mlsanity.com/paper/2112.15483/) (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)
- [Construction of Digital Terrain Maps from Multi-view Satellite Imagery using Neural Volume Rendering](https://mlsanity.com/paper/2508.01386/) (2025)
- [Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss](https://mlsanity.com/paper/2509.00835/) (2025)
- [EORestore-Agent: Fidelity-Guided Agentic Restoration of Remote Sensing Images with Composite Degradations](https://mlsanity.com/paper/2610.06196/) (2026)

## Abstract (arXiv)

Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network to solve this task and introduce the spatial attention mechanism into the remote sensing imagery cloud removal task, proposes a model named spatial attention generative adversarial network (SpA GAN), which imitates the human visual mechanism, and recognizes and focuses the cloud area with local-to-global spatial attention, thereby enhancing the information recovery of these areas and generating cloudless images with better quality...