Band Selection for Hyperspectral Image Using Principal Components Analysis and Maxima-Minima Functional

  • Kitti Koonsanit
  • Chuleerat Jaruskulchai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6746)


Nowadays, hyperspectral image software becomes widely used. Although hyperspectral images provide abundant information about bands, their high dimensionality also substantially increases the computational burden. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details. In this paper, we present band selection technical using principal components analysis (PCA) and maxima-minima functional for hyperspectral image such as small multi-mission satellite (SMMS). Band selection method in our research not only serves as the first step of hyperspectral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA and maxima-minima functional method is proposed for hyperspectral band selection. Based on tests in a SMMS hyperspectral image, this new method achieves good result in terms of robust clustering.


Band Selection Principal Components Analysis PCA Satellite image Maxima-Minima Functional 


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  1. 1.
    Richards, J.A.: Remote Sensing Digital Image Analysis: An introduction. Springer, Heidelberg (1986)CrossRefGoogle Scholar
  2. 2.
    Small Multi-Mission Satellite (SMMS) Data,
  3. 3.
    Agarwal, A., El-Ghazawi, T., El-Askary, H., Le-Moigne, J.: Efficient Hierarchical-PCA Dimension Reduction for Hyperspectral Imagery. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, December 15-18, pp. 353–356 (2007)Google Scholar
  4. 4.
    Kaewpijit, S., Le-Moige, J., El-Ghazawi, T.: Hyperspectral Imagery Dimension Reduction Using Pricipal Component Analysis on the HIVE. In: Science Data Processing Workshop, NASA Goddard Space Flight Center (February 2002)Google Scholar
  5. 5.
  6. 6.
    Cheng, X., Chen, Y.R., Tao, Y., Wang, C.Y., Kim, M.S., Lefcourt, A.M.: A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. ASAE Transactions 47(4), 1313–1320 (2004)CrossRefGoogle Scholar
  7. 7.
    Bouckaert, R.R.: WEKA Manual, WAIKATO University, pp. 1–303 (January 2010)Google Scholar
  8. 8.
    Kirkby, R., Frank, E.: Weka Explorer User Guide. University of Waikato, New Zealand (2005)Google Scholar
  9. 9.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), Google Scholar
  10. 10.
    Jackson, J.E.: A User Guide to Principal Components. John Wiley and Sons, New York (1991)CrossRefzbMATHGoogle Scholar
  11. 11.
    Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kitti Koonsanit
    • 1
  • Chuleerat Jaruskulchai
    • 1
  1. 1.Department of Computer Science, Faculty of ScienceKasetsart UniversityBangkokThailand

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