Department of Computer Engineering, Faculty of Sciences and Technology, Tangier, Route Ziaten, P.O. Box 416, Morocco
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This preprint has been withdrawn.
How to cite. Zaytar, M. A. and El Amrani, C.: OzoNet: Atmospheric Ozone Interpolation with Deep Convolutional
Neural Networks, Geosci. Instrum. Method. Data Syst. Discuss. [preprint], https://doi.org/10.5194/gi-2018-53, 2019.
Received: 02 Jan 2019 – Discussion started: 03 Jun 2019