Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis

  • Goo Jun
  • Joydeep Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

We propose a hybrid hierarchical classifier that solves multi-class problems in high dimensional space using a set of binary classifiers arranged as a tree in the space of classes. It incorporates good aspects of both the binary hierarchical classifier (BHC) and the margin tree algorithm, and is effective over a large range of (sample size, input dimensionality) values. Two aspects of the proposed classifier are empirically evaluated on two hyperspectral datasets: the structure of the class hierarchy and the classification accuracies. The proposed hybrid algorithm is shown to be superior on both aspects when compared to other binary classification trees, including both the BHC and the margin tree algorithm.

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References

  1. 1.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artifical Intelligence Research 2, 263 (1995)MATHGoogle Scholar
  2. 2.
    Kumar, S., Ghosh, J., Crawford, M.M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis & Applications 5(2), 210–220 (2002)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Tibshirani, R., Hastie, T.: Margin trees for high-dimensional classification. J. Mach. Learn. Res. 8, 637–652 (2007)MATHGoogle Scholar
  4. 4.
    Rajan, S., Ghosh, J.: An empirical comparison of hierarchical vs. two-level approaches to multiclass problems. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 283–292. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Kumar, S., Ghosh, J., Crawford, M.M.: Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Trans. on Geosci. and Remote Sens. 39(7), 1368–1379 (2001)CrossRefGoogle Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)CrossRefMATHGoogle Scholar
  7. 7.
    Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers EC-14(3), 326–334 (1965)CrossRefMATHGoogle Scholar
  8. 8.
    Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Processing Magazine 19, 17–28 (2002)CrossRefGoogle Scholar
  9. 9.
    Morgan, J.T.: Adaptive hierarchical classifier with limited training data. Ph.D thesis, Univ. of Texas at Austin (2002)Google Scholar
  10. 10.
    Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. and Remote Sens. 43(3), 492–501 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Goo Jun
    • 1
  • Joydeep Ghosh
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of Texas at AustinAustinUSA

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