Seeing Through Clouds in Satellite Images
This addresses the issue of cloud interference for applications like agriculture and disaster monitoring, but is incremental as it builds on existing cloud removal methods.
The paper tackles the problem of cloud occlusion in satellite images by using RF signals to reconstruct occluded pixels, achieving an 8dB improvement in PSNR over baselines.
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.