Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking
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Taravat-2015-Multilayer perceptro.pdf
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Author(s)
Taravat, Alireza
Peronaci, Simone
Del Frate, Fabio
Oppelt, Natascha
Proud, Simon R.
Date Issued
February 2015
Journal
Remote Sensing
Publisher
MDPI AG
Citation
Taravat, Alireza, Simon Proud, Simone Peronaci, Fabio Del Frate, and Natascha Oppelt. “Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking.” Remote Sensing 7, no. 2 (February 2015): 1529–1539.
Version
Final published version
Abstract
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
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DOI of Published Version
https://doi.org/10.3390/rs70201529
