Advertisement

From Narrow to Broad Band Design and Selection in Hyperspectral Images

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • José M. Sotoca
  • Pedro García-Sevilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

Selecting the most relevant bands from a hyperspectral image would considerably reduce the amount of data without practically losing relevant information. In addition, if some physical and signal criteria of this selection are taken into account, the obtained results grouping consecutive bands would be useful to design new filters for hyperspectral cameras in order to improve the efficiency of these devices. Starting from certain number of pre-selected bands, intervals of spectrally adjacent instances to these initial bands are considered for calculating new broader bands. Results will show how a weighted average on these intervals can keep, or even improve, the performance respecting to a narrower selection, avoiding, at the same time, common drawbacks from the narrow-band acquisition devices.

Keywords

Mutual Information Spectral Resolution Hyperspectral Image Band Selection Unknown Class 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)MATHCrossRefGoogle Scholar
  2. 2.
    Landgrebe, D.A.: Signal theory methods in multispectral remote sensing. Wiley, Chichester (2003)Google Scholar
  3. 3.
    Martinez-Uso, A., Pla, F., Sotoca, J.M., Garcia-Sevilla, P.: Clustering-based hyperspectral band selection using information measures. IEEE Trans on GRS 45(12), 4158–4171 (2007)Google Scholar
  4. 4.
    Martinez-Uso, A., Pla, F., Sotoca, J.M., Garcia-Sevilla, P.: Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 30–38. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Price, J.C.: Spectral band selection for visible-near infrared remote sensing:spectral-spatial resolution tradeoffs. IEEE Trans on GRS 35(5), 1277–1285 (1997)MathSciNetGoogle Scholar
  6. 6.
    Sun, L., Staenz, K., Neville, R., White, H.: Impact of sensor signal-to-noise ratio and spectral characteristics on hyperspectral geoscience products. In: Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on, pp. 2064–2067 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • José M. Sotoca
    • 1
  • Pedro García-Sevilla
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosJaume I Univerisity 

Personalised recommendations