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

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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.

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Hasan, C., Horne, R., Mauw, S., & Mizera, A. (2022). Cloud removal from satellite imagery using multispectral edge-filtered conditional generative adversarial networks. International Journal of Remote Sensing, 43(5), 1881–1893. https://doi.org/10.1080/01431161.2022.2048915

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