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Single Image Cloud Removal Using U-Net and Generative Adversarial Networks | IEEE Journals & Magazine | IEEE Xplore

Single Image Cloud Removal Using U-Net and Generative Adversarial Networks

Publisher: IEEE

Abstract:

Cloud removal is a ubiquitous and important task in remote sensing image processing, which aims at restoring the ground regions shadowed by clouds. It is challenging to r...View more

Abstract:

Cloud removal is a ubiquitous and important task in remote sensing image processing, which aims at restoring the ground regions shadowed by clouds. It is challenging to remove the clouds for a single satellite image due to the difficulty of distinguishing clouds from white objects on the ground and filling the irregular missing regions with visual consistency. In this article, we propose a novel two-stage cloud removal method. The first stage is cloud segmentation, i.e., extracting the clouds and removing the thin clouds directly using U-Net. The second stage is image restoration, i.e., removing the thick cloud and recovering the corresponding irregular missing regions using generative adversarial network (GAN). We evaluate the proposed scheme on both synthetic images and real satellite images (over 20\,000\, \times \,20\,000 pixels). On synthetic images for cloud coverage less than 40%, the proposed scheme achieves improvements of 0.049–0.078 in Structural SIMilarity (SSIM) and 3.8–6.2 dB in peak signal-to-noise ratio (PSNR), while the \ell _{1} -norm error reduces by 49%–78%, compared with a state-of-the-art deep learning method Pix2Pix. On real satellite images, we demonstrate the consistent visual results of the proposed scheme.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 59, Issue: 8, August 2021)
Page(s): 6371 - 6385
Date of Publication: 15 October 2020

ISSN Information:

Publisher: IEEE

References

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