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arXiv · 2011.07017

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

Paula Harder·William Jones·Redouane Lguensat·Shahine Bouabid·James Fulton·Dánell Quesada-Chacón·Aris Marcolongo·Sofija Stefanović·Yuhan Rao·Peter Manshausen·Duncan Watson-Parris
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Abstract

The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visible images. This work presents how deep learning can be applied successfully to create those images by using U-Net based architectures. The proposed methods show promising results, achieving a structural similarity index (SSIM) up to 86\% on an independent test set and providing visually convincing output images, generated from infra-red observations.

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Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánell Quesada-Chacón, Aris Marcolongo, Sofija Stefanović, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris. 2020-11-13. NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations. https://arxiv.org/abs/2011.07017

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