Harder, PaulaPaulaHarderJones, WilliamWilliamJonesLguensat, RedouaneRedouaneLguensatBouabid, ShahineShahineBouabidFulton, JamesJamesFultonQuesada-Chacón, DánellDánellQuesada-ChacónMarcolongo, ArisArisMarcolongoStefanovi?, SofijaSofijaStefanovi?Rao, YuhanYuhanRaoManshausen, PeterPeterManshausenWatson-Parris, DuncanDuncanWatson-Parris2022-03-152022-03-152020https://publica.fraunhofer.de/handle/publica/414257The 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.en003006519paper