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Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification

  • Peijun Du
  • Wei Zhang
  • Hao Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

Multiple classifier combination is used to hyperspectral remote sensing image classification in this paper, and some classifier combination algorithms are experimented. Based on a brief introduction to multiple classifier system and general algorithms, a modified evidence combination algorithm is adopted to handle evidence with high inconsistency, and a hierarchical multiple classifier system is designed to integrate the advantages of multiple classifiers. Using the OMIS hyperspectral remote sensing image as the study data, training samples manipulation approaching including boosting and bagging, together with parallel and hierarchical combination schemes are experimented. Mahalanobis distance classifier, MLPNN, RBFNN, SVM and J4.8 decision tree are selected as member classifiers. In the multiple classifier combination scheme based on training samples, both boosting and bagging can enhance the classification accuracy of any individual classifier, and boosting performs a bit better than bagging when the same classifier is used. In classification ensemble using multiple classifier combination approaches, both parallel combination using modified evidence theory and hierarchical classifier system can obtain higher accuracy than any individual member classifiers. So it can be concluded that multiple classifier combination is suitable for hyperspectral remote sensing image classification.

Keywords

Multiple classifier combination classifier ensemble evidence theory boosting bagging hyperspectral remote sensing 

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References

  1. 1.
    Ha, S., Ma, J.W., Li, Q.Q., et al.: Dimension Reduction of Self-organized Neural Network Classification for Multi-band Satellite Data. Geomatics and Information Science of Wuhan University (5), 461–466 (2004)Google Scholar
  2. 2.
    Carpenter, G.A., Gjaja, M.N., Gopal, S.: ART Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data. IEEE Transactions on Geoscience and Remote Sensing (3), 308–325 (1997)Google Scholar
  3. 3.
    Zhang, Y., Shao, M.Z.: Unmixing Based on Radial Basis Function Neural Network. Journal of Remote Sensing (7), 285–288 (2002)Google Scholar
  4. 4.
    Zhang, W., Du, P.J., Zhang, H.P.: Mixed Pixel Decomposition Based on Neural Network. Bulletin of Surveying and Mapping (7), 23–36 (2007)Google Scholar
  5. 5.
    He, M.L., Sheng, Z.Q., Kong, F.S., et al.: Study on Multi-source Remote Sensing Images Classification with SVM. Journal of Image and Graphics (4), 648–654 (2007)Google Scholar
  6. 6.
    Waske, B., Menz, G., Benediktsson, J.A.: Fusion of Support Vector Machines for Classifying SAR and Multispectral Imagery from Agricultural Areasm. In: IEEE Geoscience and Remote Sensing Symposium, pp. 4842–4845 (2007)Google Scholar
  7. 7.
    Zhang, Y.F., Zhang, L.P., Gong, J.Y., et al.: Remote Sensing Image Classification Based on Artificial Immune System. Journal of Remote Sensing (7), 374–380 (2005)Google Scholar
  8. 8.
    Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(3), 823–870 (2007)CrossRefGoogle Scholar
  9. 9.
    Steele, B.M.: Combining Multiple Classifiers: An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping. In: Remote Sensing of Environment, pp. 545–556 (2000)Google Scholar
  10. 10.
    Ahmed, A., Mohamed, D.: A New Technique for Combining Multiple Classifiers Using the Dempster-Shafer Theory of Evidence. Journal of Artificial Intelligence Research (2002)Google Scholar
  11. 11.
    Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Multiple Classifiers Applied to Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing (10), 2291–2299 (2002)Google Scholar
  12. 12.
    Bo, Y.C., Wang, J.F.: Combining Multiple Classifiers for Thematic Classification of Remotely Sensed Data. Journal of Remote Sensing (5), 555–564 (2005)Google Scholar
  13. 13.
    Zhang, X.Y., Feng, X.Z., Liu, W.: Urban Vegetation Categories Recognition by Multiple Classifier System from IKONOS Imagery. Journal of Southeast University (Natural Science Edition) (5), 399–403 (2007)Google Scholar
  14. 14.
    Foody, G.M., Boyd, D.S., Sanchez-Hernandez, C.: Mapping a specific class with an ensemble of classifiers. International Journal of Remote Sensing, 1733–1746 (2007)Google Scholar
  15. 15.
    Doan, H.T.X., Foody, G.M.: Increasing soft classification accuracy through the use of an ensemble of classifiers. International Journal of Remote Sensing (10), 4609–4623 (2007)Google Scholar
  16. 16.
    Dehghani, H., Ghassemian, H., Keshavarz, A.: A Multiple Classifier Template For Hyperspectral Images Classification. In: 2005 IEEE Geoscience and Remote Sensing Symposium, pp. 3776–3779 (2005)Google Scholar
  17. 17.
    Benediktsson, J.A., Chanussot, J., Fauvel, M.: Multiple classifiers in remote sensing: from basics to recent developments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 501–512. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Multiple Classifiers Applied to Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing 40(10), 2291–2300 (2002)CrossRefGoogle Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques, 2nd edn. China Machine Press, Beijing (2006)zbMATHGoogle Scholar
  20. 20.
    Hassiba, N., Youcef, C.: Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry & Remote Sensing (61), 125–133 (2006)Google Scholar
  21. 21.
    Liu, C.P., Dai, J.F., Zhong, W., et al.: Multi-source Remote Sensing Information Based on Fuzzy Evidence Theory. Pattern Recognition and Artificial Intelligence 6, 213–218 (2003)Google Scholar
  22. 22.
    Sun, Q., Ye, X.Q., Gu, W.K.: A New Combination Rules of Evidence Theory. Acta Electronica Sinica (8), 117–119 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peijun Du
    • 1
  • Wei Zhang
    • 1
  • Hao Sun
    • 1
  1. 1.Department of Remote Sensing and Geographical Information ScienceChina University of Mining and TechnologyXuzhou CityP.R. China

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