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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 Haralick texture features extract from GLCM as space descriptors to be combined with spectral information to improve the SVM classification algorithm, the result will be compared with Graph Cuts approach that introduce spatial domain information of the result image of spectral classification with SVM. The proposed approach is tested on common scenes of urban imagery. The experimental results show satisfactory values and are very promising.