Markovian approach using several Gibbs energy for remote sensing images segmentation

Youssef Chahir (1) , Sadia Alkama (2) , Daoud Berkani (3)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 Department of Automatics
3 National Polytechnic School Algiers

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.

Mots clés: Markov fields; Vector segmentation; Evaluation function; Remote sensing images; Gibbs energy

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Multimédia [cs.MM]

Youssef Chahir, Sadia Alkama, Daoud Berkani. Markovian approach using several Gibbs energy for remote sensing images segmentation. Analog Integrated Circuits and Signal Processing, 2011, 69 (1), pp.39 - 47. ⟨10.1007/s10470-011-9631-8⟩. ⟨hal-01882790⟩