Extraction de connaissances à partir de données multi-spectrales : cas des images MSG

Abstract

The Meteosat Second Generation (MSG) satellite transmits images of the observation of the earth every fifteen minutes in twelve different spectral bands. It allows us to follow phenomena that take place on the surface of the planet. Our work is about an important weather phenomenon is precipitation, to estimate precipitation from MSG images, most studies have not maximized the terabytes of data provided by the channels in this satellite, which are potentially rich in new resources that need to be exploited. Moreover, these studies classify pixels conventionally, where a pixel is considered either 100 % precipitant or 0 % (no-precipitant), whereas actually it cannot be classified in a clear and unambiguous way. To address this problem, we propose a method that exploits the images of the channels and constructs an estimation model in the form of fuzzy association rules to estimate the rainfall in Northeastern Algeria. Each rule is in if (condition)-then (conclusion) form, where the condition is a combination of the various fuzzy classes of MSG images, and the conclusion contains a single fuzzy class that represents the intensities of rain : no-rain, low, moderate, and high. The obtained results are compared with the data obtained by the Multi-Sensor Precipitation Estimate (MPE) product of the European Organisation for the Exploitation of Meteorological Satellites ( EUMETSAT ).