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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1068-3739
1934-8096
DOI:10.3103/S1068373919070045