SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas

  • Aissam Bekkari
  • Soufiane Idbraim
  • Azeddine Elhassouny
  • Driss Mammass
  • Mostafa El yassa
  • Danielle Ducrot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification’s algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM). For this purpose we propose a methodology allowing to combine these two informations using a combination of multi-spectral features and Haralick texture features as data source with composite kernel. The proposed approach was tested on common scenes of urban imagery. The results allow a significant improvement of the classification performances when compared with the two sets of attributes used separately. The experimental results indicate an accuracy value of 93.29% which is very promising.


SVM composite kernel Haralick features Satellite image Spatial and spectral information GLCM 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aissam Bekkari
    • 1
  • Soufiane Idbraim
    • 1
  • Azeddine Elhassouny
    • 1
  • Driss Mammass
    • 1
  • Mostafa El yassa
    • 1
  • Danielle Ducrot
    • 2
  1. 1.IRF – SIC Laboratory, Faculty of SciencesIbn Zohr UniversityAgadirMorocco
  2. 2.CesbioToulouse Cedex 9France

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