Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking
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Remote Sensing & Environmental Modelling Lab, Kiel University, 24098 Kiel, Germany
2
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), MA 02139, USA
3
Department of Civil Engineering and Computer Science Engineering (D.I.C.I.I), University of Rome "Tor Vergata", 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Alexander A. Kokhanovsky, Richard Müller and Prasad S. Thenkabail
Remote Sens. 2015, 7(2), 1529-1539; https://doi.org/10.3390/rs70201529
Received: 29 September 2014 / Accepted: 29 January 2015 / Published: 2 February 2015
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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.
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Keywords:
Multilayer perceprton; Neural networks; Cloud masking; SEVIRI; EUMETSAT
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MDPI and ACS Style
Taravat, A.; Proud, S.; Peronaci, S.; Del Frate, F.; Oppelt, N. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sens. 2015, 7, 1529-1539.
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