OSA's Digital Library

Chinese Optics Letters

Chinese Optics Letters


  • Vol. 9, Iss. 1 — Jan. 10, 2011
  • pp: 011003–

Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classif ication

Kun Tan and Peijun Du  »View Author Affiliations

Chinese Optics Letters, Vol. 9, Issue 1, pp. 011003- (2011)

View Full Text Article

Acrobat PDF (1527 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

  • Export Citation/Save Click for help


Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer II hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.

© 2011 Chinese Optics Letters

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.7410) Image processing : Wavelets
(100.4145) Image processing : Motion, hyperspectral image processing

Kun Tan and Peijun Du, "Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classif ication," Chin. Opt. Lett. 9, 011003- (2011)

Sort:  Author  |  Year  |  Journal  |  Reset


  1. G. Camps-Valls and L. Bruzzone, IEEE Trans. Geosci. Remote Sensing 43, 1351 (2005).
  2. D. Landgrebe, IEEE Signal Processing Magazine 19, (1) 17 (2002).
  3. K. Tan and P. Du, Spectrosc. Spectral Anal. 28, 2009 (2008).
  4. G. F. Hughes, IEEE Trans. Information Theory 14, 55 (1968).
  5. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mar?, J. Vila-Frances, and J. Calpe-Maravilla, IEEE Geosci. Remote Sensing Lett. 3, 93 (2006).
  6. Y. Bazi and F. Melgani, IEEE Trans. Geosci. Remote Sensing 44, 3374 (2006).
  7. G. M. Foody and A. Mathur, Remote Sensing of Environment 93, 107 (2004).
  8. F. Melgani and L. Bruzzone, IEEE Trans. Geosci. Remote Sensing 42, 1778 (2004).
  9. K. Tan and P. J. Du, J. Infrared Millim Waves 27, 123 (2008).
  10. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mar?, J. L. Rojo-Alvarez, and M. Mart?nez-Ramon, IEEE Trans. Geosci. Remote Sensing 46, 1822 (2008).
  11. S. Arivazhagan, L. Ganesan, and S. P. Priyal, Pattern Recognition Lett. 27, 1976 (2006).
  12. Y. O. Ouma, J. Tetuko, and R. Tateishi, Int. J. Remote Sensing 29, 3417 (2008).
  13. S. Arivazhagan and L. Ganesan, Pattern Recognition Lett. 24, 3197 (2003).
  14. S. R. Garrity, L. A. Vierling, A. M. S. Smith, M. J. Falkowski, and D. B. Hann, Can. J. Remote Sensing 34, S376 (2008).
  15. X. Huang, L. Zhang, and P. Li, Photogramm. Eng. Remote Sensing 74, 1585 (2008).
  16. G. Mercier, G. Moser, and S. B. Serpico, IEEE Trans. Geosci. Remote Sensing 46, 1428 (2008).
  17. L. Gueguen and M. Datcu, IEEE Trans. Geosci. Remote Sensing. 45, 827 (2007).
  18. I. Daubechies, IEEE Trans. Information Theory 36, 961 (1990).
  19. L. Jiang, Y. Luo, J. Zhao, and T. Zhuang, Chin. Opt. Lett. 6, 495 (2008).
  20. Y. Wang, T. Liu, and J. Jiang, Chin. Opt. Lett. 6, 657 (2008).
  21. Z. Du, X. Jin, and Y. Yang, HVAC&R Res. 14, 959 (2008).
  22. B. Scholkopf, C. Burges, and A. Smola, (eds.) Advances in Kernel Methods: Support Vector Learning (MIT Press, Cambridge, 1999).
  23. V. N. Vapnik, Statistical Learning Theory (Springer, New York, 1998).
  24. S. G. Mallat, IEEE Trans. Pattern Anal. Machine Intell. 11, 674 (1989).
  25. W. Y. Ma and B. S. Manjunath, in Proceedings of the IEEE Int. Conf. Image Processing 256 (1995).
  26. Z.-Z. Wang and J.-H. Yong, IEEE Trans. Image Processing 17, 1421 (2008).
  27. S. Borah, E. L. Hines, and M. Bhuyan, J. Food Eng. 79, 629 (2007).
  28. V. Manian and R. Vasquez, Pattern Recognition 31, 1937 (1998).

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited