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Classification of high resolution urban satellite Images combining SVM and Graph Cuts

A. Bekkari S. Idbraim K. Housni D. Mammass Youssef Chahir 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : The classification of remotely sensed images knows a large progress seen the availability of images of different resolutions as well as the abundance of the techniques of classification. Moreover a number of works showed promising results by the fusion of spatial and spectral information. For this purpose we propose a methodology allowing to combine this two information to refine an SVM classification, The approach uses graph cuts to improve the SVM algorithm, as graph cuts introduce spatial domain information of the image that is lacking in the SVM. The proposed approach is tested on common scenes of urban imagery. The experimental results show satisfactory values and are very promising.
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A. Bekkari, S. Idbraim, K. Housni, D. Mammass, Youssef Chahir. Classification of high resolution urban satellite Images combining SVM and Graph Cuts. 2010 5th International Symposium On I/V Communications and Mobile Network (ISVC), Sep 2010, Rabat, France. ⟨10.1109/ISVC.2010.5656433⟩. ⟨hal-01882847⟩

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