Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

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Mounir Sehad 1,* Mourad Lazri 2 Soltane Ameur 2 Jean Michel Brucker 2 Fethi Ouallouche 2

1. Laboratory for analysis and modeling random phenomena (LAMPA), University of Tizi Ouzou, 15000, Tizi Ouzou, Algeria

2. School of engineers (EPMI), EPMI - 13 Boulevard de l'Hautil 95092, Cergy Pontoise, Cedex, Paris, France

* Corresponding author.


Received: 21 Feb. 2014 / Revised: 9 Apr. 2014 / Accepted: 10 May 2014 / Published: 8 Jun. 2014

Index Terms

Classification, Artificial neural network, Radar, MSG image


This paper provides a new method for the classification of rainfall areas in convective and stratiform rain using MSG/SEVIRI (Spinning Enhanced Visible and Infrared) data. The proposed approach is based on spectral and temporal properties of clouds. The spectral parameters used are: brightness temperature (BT) and brightness temperature differences (BTDs), and the temporal parameter (RCT10.8) is the rate of change of (BT) in the 10.8µm channel over two consecutive images. The developed rain area classification technique (RACT-DN) is based on two multilayer perceptron neural networks (MLP-D for daytime and MLP-N for nighttime) which relies on the correlation of satellite data with convective and stratiform rain. The two algorithms (MLP-D and MLP-N) are trained using as reference data from ground meteorological radar over northern Algeria. The results show that RACT-DN classifier gives accurate discrimination between convective and stratiform areas during daytime and nighttime.

Cite This Paper

Mounir Sehad, Mourad Lazri, Soltane Ameur, Jean Michel Brucker, Fethi Ouallouche,"Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data", IJIGSP, vol.6, no.7, pp.28-35, 2014. DOI: 10.5815/ijigsp.2014.07.04


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