NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations (Papers Track)
Paula Harder (Fraunhofer ITWM); William Jones (University of Oxford); Redouane Lguensat (LSCE-IPSL); Shahine Bouabid (University of Oxford); James Fulton (University of Edinburgh); Dánnell Quesada-Chacón (Technische Universität Dresden); Aris Marcolongo (University of Bern); Sofija Stefanovic (University of Oxford); Yuhan Rao (North Carolina Institute for Climate Studies); Peter Manshausen (University of Oxford); Duncan Watson-Parris (University of Oxford)
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.