Cloud removal from satellite imagery using multispectral edge-filtered conditional generative adversarial networks

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Hasan, Cengis and Horne, Ross and Mauw, Sjouke and Mizera, Andrzej (2022) Cloud removal from satellite imagery using multispectral edge-filtered conditional generative adversarial networks. International Journal of Remote Sensing, 43 (5). pp. 1881-1893. ISSN 0143-1161 (https://doi.org/10.1080/01431161.2022.2048915)

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Abstract

We propose a Generative Adversarial Network (GAN) based architecture for removing clouds from satellite imagery. Data used for training comprises of visible light RGB and near-infrared (NIR) band images. The novelty lies in the structure of the discriminator in the GAN architecture, which compares generated and target cloud-free RGB images concatenated with their edge-filtered versions. Experimental results show that our approach to removing clouds outperforms both visually and according to metrics, a benchmark solution that does not take edge filtering into account, and that improvements are robust when varying both training dataset size and NIR cloud penetrability.

ORCID iDs

Hasan, Cengis, Horne, Ross ORCID logoORCID: https://orcid.org/0000-0003-0162-1901, Mauw, Sjouke and Mizera, Andrzej;
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