Combination of Spectral and Textural Features in the MSG Satellite Remote Sensing Images for Classifying Rainy Area into Different Classes

The rainfall intensity classification technique using spectral and textural features from MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared) satellite data is proposed in this paper. The study is carried out over north of Algeria. The developed method is based on the artificial neural multilayer perceptron network (MLP). Two MLP algorithms are used: the MLP-S based only on spectral parameters and the MLP-ST that use both spectral and textural features. The MLP model is created with three layers (input, hidden, and output) that consist of 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 and 10 spectral input neurons for the MLP-S and 15 input neurons for MLP-ST, which as ten spectral features that were calculated from MSG thermal infrared brilliance temperature and brilliance temperature difference and as five textural features, and The rainfall intensity areas classified by the proposed technique are validated against ground-based radar data. The rainfall rates used in the training set are derived from Setif radar measurements (Algeria). The results obtained after applying this method show 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. These results are compared with results obtained with the probability of rainfall intensity (PRI). This comparison revealed a clear outperformance of the MLP algorithms over the PRI algorithms. Best results are provided by the MLP-ST algorithm. The combination of spectral and textural features in the MSG-SEVIRI imagery is important and for the classification of the rainfall intensities to different classes.