Preprints
https://doi.org/10.5194/gi-2018-53
https://doi.org/10.5194/gi-2018-53
03 Jun 2019
 | 03 Jun 2019
Status: this preprint has been withdrawn by the authors.

OzoNet: Atmospheric Ozone Interpolation with Deep Convolutional Neural Networks

Mohamed Akram Zaytar and 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.

This preprint has been withdrawn.

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Mohamed Akram Zaytar and Chaker El Amrani

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
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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