Using Convolutional Neural Networks for Cloud Detection from Meteor-M No. 2 MSU-MR Data

• Published in: Russian meteorology and hydrology 2019-07, Vol.44 (7), p.459-466
• Main Authors: Andreev, A. I., Shamilova, Yu. A., Kholodov, E. I.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Atmospheric Sciences; Cloud computing; Cloud detection; Clouds; Detection; Earth and Environmental Science; Earth Sciences; Learning algorithms; Machine learning; Meteorology; Meteors; Neural networks; Satellite imagery; Satellites; Spaceborne remote sensing; Spectroradiometers
• ISSN: 1068-3739; 1934-8096
• DOI: 10.3103/S1068373919070045

A method for cloud detection using the machine-learning algorithm based on a convolutional neural network is presented. Input data are satellite images received from the MSU-MR multispectral low-resolution scanning unit onboard the Meteor-M No. 2 satellite. The developed method can be an alternative to the traditional algorithms of cloud detection based on the calculation of differential indices and thresholds. The algorithm is verified using the machine-learning metrics, comparing the resulting cloud mask with the reference one obtained by interpreting the satellite image by an experienced meteorologist. It was also compared (for verification) with a similar product based on VIIRS spectroradiometer data. The cloud mask computed using the algorithm allows the automatic thematic processing of satellite images.