2021/06/15 by M. Zhao, Zhao, Mingmin, Peder A. Olsen +3 · 2 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.08408
openalex publication_date 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multi-modal multi-temporal cloud removal model. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system significantly outperforms baselines by 8dB in PSNR. We also demonstrate use cases of our system in digital agriculture, flood monitoring, and wildfire detection. We will release the processed dataset to facilitate future research.