Development of a hybrid classification technique based on deep learning applied to MSG / SEVIRI multispectral data. (15th October 2019)

• Record Type: Journal Article
• Authors: Oukali, Salim; Lazri, Mourad; Labadi, Karim; Brucker, Jean Michel; Ameur, Soltane
• Is Part Of: Journal of atmospheric and solar-terrestrial physics. Volume 193(2019)
• Publication Date: 2019-10-15
• Subjects: MSG satellite -- Classification -- Deep learning -- CNN -- MLP; Geophysics -- Periodicals; Atmospheric physics -- Periodicals; Géophysique -- Périodiques; Météorologie physique -- Périodiques; Electronic journals; 551.51
• DOI: 10.1016/j.jastp.2019.105062
• Languages: English
• ISSNs: 1364-6826

Abstract

Abstract: The approach developed in this paper for the classification of precipitation intensities is based on deep learning of neural network. Multispectral data from the MSG satellite (Meteosat Second Generation) providing information about the cloud's physical and optical characteristics are exploited and used as inputs to a deep neural network model. The model is a combination of CNN (Convolutional Neural Network) and DMLP (Deep Multi-Layer Peceptron) which is learned and validated by comparison with the corresponding Radar data during the rainy seasons 2006/2007 and 2010/2011 respectively. The CNN extracts spatial characteristics from MSG multi-spectral images. Then, the set of spatial and multi-spectral information are used as inputs for the DMLP. The results show an improvement compared to the three other classifiers (Random Forest, Support Vector Machine and Artificial Neural Network). The CNN-DMLP method was also compared to the technique combining the three classifiers (SAR). The results indicate a percentage correct (PC) of 97% and a probability of detection (POD) of 90% for CNN-DMLP method compared to 94% and 87% for of the SAR technique, respectively. In terms of bias, the CNN-DMLP method gives 1.08 compared 1.10 for SAR technique. Highlights: Development of a hybrid model based on deep learning of neural network. Learning of the hybrid model for classification of clouds by comparison between MSG data and Radar data. Application of the model to MSG data for the classification. Validation of the model by comparison with the corresponding Radar data. Inter-Comparison of the hybrid model with ANN, SVM and RF.