# Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions

arXiv: https://arxiv.org/abs/2506.17885 · HTML: https://mlsanity.com/paper/2506.17885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery](https://mlsanity.com/paper/2009.07683/) (2020)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion](https://mlsanity.com/paper/2206.02850/) (2022)
- [NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function](https://mlsanity.com/paper/2108.11573/) (2021)

## Built on by (most cited first, 1 of 1)

- [IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal](https://mlsanity.com/paper/2606.09347/) (2026)

## Abstract (arXiv)

Cloud contamination significantly impairs the usability of optical satellite imagery, affecting critical applications such as environmental monitoring, disaster response, and land-use analysis. This research presents a Cloud-Attentive Reconstruction Framework that integrates SAR-optical feature fusion with deep learning-based image reconstruction to generate cloud-free optical imagery. The proposed framework employs an attention-driven feature fusion mechanism to align complementary structural information from Synthetic Aperture Radar (SAR) with spectral characteristics from optical data. Furthermore, a cloud-aware model update strategy introduces adaptive loss weighting to prioritize cloud-occluded regions, enhancing reconstruction accuracy. Experimental results demonstrate that the proposed method outperforms existing approaches, achieving a PSNR of 31.01 dB, SSIM of 0.918, and MAE of 0.017. These outcomes highlight the framework's effectiveness in producing high-fidelity, spatially and spectrally consistent cloud-free optical images.