Textural Features for Hyperspectral Pixel Classification

  • Olga Rajadell
  • Pedro García-Sevilla
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5524)


Hyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the spectrum and the possibility of distinguish subtle differences in the image. For this reason, the process of band selection to reduce redundant information is highly recommended to deal with them. Band selection methods pursue the reduction of the dimension of the data resulting in a subset of bands that preserves the most of information. The accuracy is given by the classification performance of the selected set of bands. Usually, pixel classification tasks using grey level values are used to validate the selection of bands. We prove that by using textural features, instead of grey level information, the number of hyperspectral bands can be significantly reduced and the accuracy for pixel classification tasks is improved. Several characterizations based on the frequency domain are presented which outperform grey level classification rates using a very small number of hyperspectral bands.


Grey Level Textural Feature Hyperspectral Image Wavelet Packet Wavelet Decomposition 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chang, T., Kuo, C.C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2, 429–441 (1993)CrossRefGoogle Scholar
  2. 2.
    Freeware Multispectral Image Data Analysis System,
  3. 3.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic, New York (1990)zbMATHGoogle Scholar
  4. 4.
    Hughes, G.F.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)CrossRefGoogle Scholar
  5. 5.
    Jimenez, L.O., Landgrebe, D.A.: Supervised classification in highdimensional space: Geometrical, statistical, and symptotically properties of multivariate data. IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. 28(1), 39–54 (1998)CrossRefGoogle Scholar
  6. 6.
    Landgrebe, D.A.: Signal Theory Methods in Multispectral Remote Sensing. Wiley, Hoboken (2003)CrossRefGoogle Scholar
  7. 7.
    Martínez-Usó, A., Pla, F., Sotoca, J.M., García-Sevilla, P.: Clustering-based Hyperspectral Band selection using Information Measures. IEEE Transactions on Geoscience & Remote Sensing 45(12), 4158–4171 (2007)CrossRefGoogle Scholar
  8. 8.
    Petrou, M., García-Sevilla, P.: Image Processing: Dealing with Texture. John Wiley & Sons, Chichester (2006)CrossRefGoogle Scholar
  9. 9.
    Richards, J., Jia, X.: Remote Sensing Digital Image Analysis, 3rd edn. Springer, Berlin (1999)CrossRefGoogle Scholar
  10. 10.
    Serpico, S.B., Bruzzone, L.: A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans. Geosci. Remote Sens. 39(7), 1360–1367 (1994)CrossRefGoogle Scholar
  11. 11.
    Shaw, G., Manolakis, D.: Signal processing for hyperspectral image explotation. IEEE Signal Process. Mag. 19(1), 12 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Olga Rajadell
    • 1
  • Pedro García-Sevilla
    • 1
  • Filiberto Pla
    • 1
  1. 1.Depto. Lenguajes y Sistemas InformáticosJaume I UniversityCastellónSpain

Personalised recommendations