Rainfall Intensity Classification Method Based on Textural and Spectral Parameters from MSG-SEVIRI

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In this paper we propose the rainfall intensity classification algorithm .The study is carried out over north of Algeria. The developed rain intensities classification technique (RICT) is based on the artificial neural multilayer perceptron network (MLP). The (MLP) model is created with three layers (input, hidden, and output) that consist of 15 input neurons, which as ten spectral features that were calculated from MSG (Meteosat Second Generation satellite) thermal infrared brilliance temperature (BT) and brilliance temperature difference (BTD) and as five textural features, and 6 output neurons in the output layer that represent the 6 rain intensities classes: very high, moderate to high, moderate, light to moderate, light and no rain. The precipitation process areas and the rainfall intensity subareas classified by the proposed technique are validated against groundbased radar data. The rainfall rates used in the training set are derived from Setif radar measurements (Algeria). The results obtained after applying this method for the north of Algeria zone show the neural network performs very well and indicate an encouraging performance of the new algorithm concerning rain area classification using MSG SEVIRI. We found that the introduction of textural parameters as additional information works in improving the classification of different rainfall intensities pixels in the MSG–SEVIRI imagery compared to the techniques based only on spectral information.

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Artificial Neural Network; Convective and Stratiform Cloud; Image Classification; Radar Data; Rainfall Intensities; Satellite Image; Spectral and Textural Features

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