Next Article in Journal
Land Subsidence over Oilfields in the Yellow River Delta
Next Article in Special Issue
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images
Previous Article in Journal
Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia
Previous Article in Special Issue
The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics
Open AccessArticle

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

1
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. View Full-Text
Keywords: Multilayer perceprton; Neural networks; Cloud masking; SEVIRI; EUMETSAT Multilayer perceprton; Neural networks; Cloud masking; SEVIRI; EUMETSAT
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop