NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
Computer Vision and Pattern Recognition
2020-12-09 v2 Machine Learning
Image and Video Processing
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.
Keywords
Cite
@article{arxiv.2011.07017,
title = {NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations},
author = {Paula Harder and William Jones and Redouane Lguensat and Shahine Bouabid and James Fulton and Dánell Quesada-Chacón and Aris Marcolongo and Sofija Stefanović and Yuhan Rao and Peter Manshausen and Duncan Watson-Parris},
journal= {arXiv preprint arXiv:2011.07017},
year = {2020}
}