Advertisement

Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • Jose M. Sotoca
  • Pedro García-Sevilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

Different methods have been proposed in order to deal with the huge amount of information that hyperspectral applications involve. This paper presents a comparison of some of the methods proposed for band selection. A relevant and recent set of methods have been selected that cover the main tendencies in this field. Moreover, a variant of an existing method is also introduced in this work. The comparison criterion used is based on pixel classification tasks.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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
  2. 2.
    Chang, C.I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Plenum, New York (2003)Google Scholar
  3. 3.
    Chang, C.I.: Target signature-constrained mixed pixel classification for hyperspectral imagery. IEEE Trans. on GRS 40(5), 1065–1081 (2002)Google Scholar
  4. 4.
    Chang, C.I., Du, Q., Sun, T.L., Althouse, M.L.G.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. on GRS 7(6), 2631–2641 (1999)Google Scholar
  5. 5.
    Chang, C.I., Wang, S.: Constrained band selection for hyperspectral imagery. IEEE Trans. on GRS 44(6), 1575–1585 (2006)Google Scholar
  6. 6.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)zbMATHGoogle Scholar
  7. 7.
    Frost III., O.L.: An algorithm for linearly constrained adaptive array processing. Proc. IEEE 60(8), 926–935 (1972)CrossRefGoogle Scholar
  8. 8.
    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, Part C 28(1), 39–54 (1998)CrossRefGoogle Scholar
  9. 9.
    Jimenez, L.O., Landgrebe, A.: Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Trans. on GRS 37(6), 2653–2667 (1999)Google Scholar
  10. 10.
    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
  11. 11.
    Martinez-Uso, A., Pla, F., Sotoca, J.M., Garcia-Sevilla, P.: Clustering-based multispectral band selection using mutual information. In: ICPR, pp. 760–763 (2006)Google Scholar
  12. 12.
    Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, Chichester (2002)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • Jose M. Sotoca
    • 1
  • Pedro García-Sevilla
    • 1
  1. 1.Dept. Lenguajes y Sistemas Informáticos, Jaume I University, Campus Riu Sec s/n 12071 CastellónSpain

Personalised recommendations