Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • Pedro García-Sevilla
  • J. M. Sotoca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Feature selection and dimensionality reduction are crucial research fields in pattern recognition. This work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure with the multispectral bands. Moreover, a criterion function is used to choose automatically the number of final clusters. Experimental results show that the method provides a very suitable subset of multispectral bands for pixel classification purposes.


Feature Selection Mutual Information Feature Subset Near Neighbour Multispectral Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Beaulieu, J.-M., Goldberg, M.: Hierarchy in picture segmentation: A stepwise optimization approach. IEEE Transactions on PAMI 11, 150–163 (1989)Google Scholar
  2. 2.
    Bruzzonne, L., Roli, F., Serpico, S.B.: An extension to multiclass cases of the jeffreys-matusita distance. IEEE Trans. on GRS 33, 1318–1321 (1995)Google Scholar
  3. 3.
    Dhillon, I., Mallela, S., Kumar, R.: A divisive information-theoretic feature clustering algorithm for text classification. JMLR 3, 1265–1287 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Ding, C., He, X.: Cluster merging and splitting in hierarchical clustering algorithms. In: ICDM 2002, vol. 1, pp. 139–146 (2002)Google Scholar
  5. 5.
    Dosil, R., Fdez-Vidal, X.R., Pardo, X.M.: Dissimilarity measures for visual pattern partitioning. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 287–294. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar
  7. 7.
    Jimenez, L., Landgrebe, D.: Supervised classification in high dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data. IEEE Transactions on System, Man, and Cybernetics 28 Part C(1), 39–54 (1998)Google Scholar
  8. 8.
    Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: In Proceedings of 7th European Conference on Machine Learning, Catania, Italy, pp. 171–182 (1994)Google Scholar
  9. 9.
    Kumar, S., Ghosh, J., Crawford, M.M.: Best basis feature extraction algorithms for classification of hyperspectral data. IEEE Trans on GRS 39(7), 1368–1379 (2001)Google Scholar
  10. 10.
    Sharon, E., Brandt, A., Basri, R.: Fast multiscale image segmentation. CVPR 1, 70–77 (2000)Google Scholar
  11. 11.
    Tourssari, G.D., Frederick, E.D., Markey, M.K., Floyd Jr., C.E.: Applications of mutual information criterion for feature selection in computer-aided diagnosis. Machine Learning Research 3, 2394–2402 (2001)Google Scholar
  12. 12.
    Vailaya, A., Figueiredo, M., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10, 117–130 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. American Statistical Association 58(301), 236–244 (1963)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • Pedro García-Sevilla
    • 1
  • J. M. Sotoca
    • 1
  1. 1.LSI DepartmentJaume I UniversityCastellónSpain

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