Advertisement

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)

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.

Keywords

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

References

  1. 1.
    Samson, C.: Contribution à la classification des images satellitaires par approche variationnelle et équations aux dérivées partielles: Thesis of doctorate, University of Nice-Sophia Antipolis (2000)Google Scholar
  2. 2.
    Townshend, J.R.G.: Land cover. International Journal of Remote Sensing 13, 1319–1328 (1992)CrossRefGoogle Scholar
  3. 3.
    Hall, F.G., Townshend, J.R., Engman, E.T.: Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment 51, 138–156 (1995)CrossRefGoogle Scholar
  4. 4.
    Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28, 823–870 (2007)CrossRefGoogle Scholar
  5. 5.
    Pal, M., Mather, P.M.: Support vector machines for classification in remote sensing. International Journal of Remote Sensing 26, 1007–1011 (2005)CrossRefGoogle Scholar
  6. 6.
    Zhu, G., Blumberg, D.G.: Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment 80, 233–240 (2002)CrossRefGoogle Scholar
  7. 7.
    Scholkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., et al.: Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45, 2758–2765 (1997)CrossRefGoogle Scholar
  8. 8.
    Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, Barcelona Spain (2007)Google Scholar
  9. 9.
    Chiu, W.Y., Couloigner, I.: Evaluation of incorporating texture into wetland mapping from multispectral images. University of Calgary, Department of Geomatics Engineering, Calgary, Canada, EARSeL eProceedings (2004)Google Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems Man and Cybernetics (1973)Google Scholar
  11. 11.
    Weszka, J.S., Dyer, C.R., Rosenfeld, A.: A Comparative Study of Texture measures for Terrain Classification. IEEE Transactions on Systems Man and Cybernetics (1976)Google Scholar
  12. 12.
    Conners, R.W., Harlow, C.A.: A Theoretical Comaprison of Texture Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence (1980)Google Scholar
  13. 13.
    Arvis, V., Debain, C., Berducat, M., Benassi, A.: Generalization of the cooccurrence matrix for colour images: application to colour texture classification. Journal Image Analysis and Stereology 23, 63–72 (2004)CrossRefGoogle Scholar
  14. 14.
    Aseervatham, S.: Apprentissage à base de Noyaux Sémantiques pour le traitement de données textuelles: Thesis of doctorate, University of Paris 13 –Galilée Institut Laboratory of Data processing of Paris Nord (2007)Google Scholar
  15. 15.
    Bousquet, O.: Introduction au Support Vector Machines (SVM). Center mathematics applied, polytechnique school of Palaiseau (2001), http://www.math.u-psud.fr/~blanchard/gtsvm/index.html
  16. 16.
    Fauvel, M., Chanussot, J., Benediktsson, J.A.: A Combined Support Vector Machines Classification Based on Decision Fusion. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2006, Denver, USA (2006)Google Scholar
  17. 17.
    Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., Vila-Francés, J., Calpe-Maravilla, J.: Composite kernels for hyperspectral image classification. IEEE Geoscience Remote Sensing Letters 3(1), 93–97 (2006)CrossRefGoogle Scholar

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

Personalised recommendations