التفاصيل البيبلوغرافية
| العنوان: |
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations |
| المؤلفون: |
Harder, Paula, Jones, William, Lguensat, Redouane, Bouabid, Shahine, Fulton, James, Quesada-Chacón, Dánell, Marcolongo, Aris, Stefanović, Sofija, Rao, Yuhan, Manshausen, Peter, Watson-Parris, Duncan |
| سنة النشر: |
2020 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2011.07017 |
| رقم الانضمام: |
edsarx.2011.07017 |
| قاعدة البيانات: |
arXiv |