NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

• URL: http://arxiv.org/abs/2011.07017v2
• Date: Tue, 8 Dec 2020 15:43:52 GMT
• Title: NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
• Authors: Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, D\'anell Quesada-Chac\'on, Aris Marcolongo, Sofija Stefanovi\'c, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris
• Abstract summary: This work presents how deep learning can be applied successfully to create visible 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.
• Score: 0.6127835361805833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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