Markovian approach using several Gibbs energy for remote sensing images segmentation

The high resolution multispectral imagery needs to be segmented into regions that can be easily interpreted and which correspond roughly to the “ground truth”. In this paper, we segment multispectral images MSG2, provided by meteorological satellite “Meteosat Second Generation 2”, by using an approach based on support vector Markov model witch takes into account both the spectral and the spatial information. A multi-variable Gaussian distribution is used in image processing and the Gibbs energy is used to describe the process of labeling. There are several forms of Gibbs energy. We test the best known of them and evaluate the different results using the Borsotti function which is known to be more appropriate with our visual perception.