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NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations (2011.07017v2)

Published 13 Nov 2020 in cs.CV, cs.LG, and eess.IV

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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