the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
OzoNet: Atmospheric Ozone Interpolation with Deep Convolutional Neural Networks
Mohamed Akram Zaytar
Chaker El Amrani
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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Mohamed Akram Zaytar and Chaker El Amrani
Interactive discussion


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RC1: 'Reviewer comments', Anonymous Referee #1, 04 Jul 2019
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EC1: 'Review comment by handling associate editor', Walter Schmidt, 09 Aug 2019
Interactive discussion


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RC1: 'Reviewer comments', Anonymous Referee #1, 04 Jul 2019
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EC1: 'Review comment by handling associate editor', Walter Schmidt, 09 Aug 2019
Mohamed Akram Zaytar and Chaker El Amrani
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
Mohamed Akram Zaytar and Chaker El Amrani
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