SVM and haralick features for classification of high resolution satellite images from urban areas

  • Authors:

  • Aissam Bekkari;Soufiane Idbraim;Azeddine Elhassouny;Driss Mammass;Mostafa El yassa;Danielle Ducrot

  • Affiliations:

  • IRF --- SIC Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco;IRF --- SIC Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco;IRF --- SIC Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco;IRF --- SIC Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco;IRF --- SIC Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco;Cesbio, Toulouse Cedex 9, France

  • Venue:
  • ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
  • Year:
  • 2012

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Abstract

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.