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.
Speakers: Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánnell Quesada-Chacón, Aris Marcolongo, Sofija Stefanovic, Yuhan Rao, Peter Mannshausen, Duncan Watson-Parris