the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
OzoNet: Atmospheric Ozone Interpolation with Deep Convolutional Neural Networks
Abstract. We propose a deep learning method for Atmospheric Ozone Interpolation. Our method directly learns an end-to-end mapping between classically interpolated satellite ozone images and the real ozone measurements. The model's architecture represents a deep stack of convolutions (CNN) that takes the already interpolated images (Using the classical state-of-the-art interpolation method) as Input and outputs a more precise Interpolation of the Region of Interest. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art interpolation quality, and achieves optimal data processing latency (∆T) for production-ready near-real-time Atmospheric Image Interpolation, which has a big advantage over the state of the art classical interpolation algorithms. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. This method showcases the potential applications of deep learning in Remote Sensing and Climate Science.
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Interactive discussion
- RC1: 'Reviewer comments', Anonymous Referee #1, 04 Jul 2019
- EC1: 'Review comment by handling associate editor', Walter Schmidt, 09 Aug 2019
Interactive discussion
- RC1: 'Reviewer comments', Anonymous Referee #1, 04 Jul 2019
- EC1: 'Review comment by handling associate editor', Walter Schmidt, 09 Aug 2019
Data sets
Bicubic Denoised Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW
Automatically Mapped Data Points on Morocco M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW
Artifically Noised Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW
CSoTA Interpolated Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW
Model code and software
Remote Sensing Noise Generation with GANs M. Akram Zaytar and C. El Amrani https://doi.org/10.5281/zenodo.3235410
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Mohamed Akram Zaytar
Chaker El Amrani
This preprint has been withdrawn.
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