Skip to main content

Spectral Content Characterization for Efficient Image Detection Algorithm Design


This paper presents spectral characterization for efficient image detection using hyperspectral processing techniques. We investigate the relationship between the number of used bands and the performance of the detection process in order to find the optimal number of band reductions. The band reduction significantly reduces computation and implementation complexity of the algorithms. Specifically, we define and characterize the contribution coefficient for each band. Based on the coefficients, we heuristically select the required minimum bands for the detection process. We have shown that the small number of bands is efficient for effective detection. The proposed algorithm is suitable for low-complexity and real-time applications.


  1. Boggs T, Gomez RB: Fast hyperspectral data processing methods. Geo-Spatial Image and Data Exploitation II, April 2001, Orlando, Calif, USA, Proceedings of SPIE 4383: 74–78.

    Article  Google Scholar 

  2. Gomez RB, Lewis AJ: On-board processing for spectral remote sensing. ISPRS Special Session Future Intelligent Earth Observing Satellites (FIEOS '02), November 2002, Denver, Colo, USA

    Google Scholar 

  3. Chai SM, Gentile A, Lugo-Beauchamp WE, Fonseca J, Cruz-Rivera JL, Wills DS: Forcal-plane processing architectures for real-time hyperspectral image processing. Applied Optics 2000,39(5):835-849. 10.1364/AO.39.000835

    Article  Google Scholar 

  4. Shaw GA, Burke HK: Spectral imaging for remote sensing. Lincoln Laboratory Journal 2003,14(1):3-28.

    Google Scholar 

  5. Nascimento SMC, Ferreira FP, Foster DH: Statistics of spatial cone-excitation ratios in natural scenes. Journal of the Optical Society of America A 2002,19(8):1484-1490. 10.1364/JOSAA.19.001484

    Article  Google Scholar 

  6. Gonzalez RC, Woods RE: Digital Image Processing. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.

    Google Scholar 

  7. Bakker WH, Schmidt KS: Hyperspectral edge filtering for measuring homogeneity of surface cover types. ISPRS Journal of Photogrammetry and Remote Sensing 2002,56(4):246-256. 10.1016/S0924-2716(02)00060-6

    Article  Google Scholar 

  8. Nischan ML, Joseph RM, Libby JC, Kerekes JP: Active spectral imaging. Lincoln Laboratory Journal 2003,14(1):131-144.

    Google Scholar 

  9. Griffin MK, Burke HK: Compensation of hyperspectral data for atmospheric effects. Lincoln Laboratory Journal 2003,14(1):29-54.

    Google Scholar 

  10. Abousleman GP, Marcellin MW, Hunt BR: Hyperspectral image compression using entropy-constrained predictive trellis coded quantization. IEEE Transactions on Image Processing 1997,6(4):566-573. 10.1109/83.563321

    Article  Google Scholar 

  11. Keshava N: Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on Geoscience and Remote Sensing 2004,42(7):1552-1565.

    Article  Google Scholar 

  12. Bajcsy P, Groves P: Methodology for hyperspectral band selection. Photogrammetric Engineering and Remote Sensing 2004,70(7):793-802.

    Article  Google Scholar 

  13. Kumar S, Ghosh J, Crawford MM: Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 2001,39(7):1368-1379. 10.1109/36.934070

    Article  Google Scholar 

  14. Girouard G, Bannari A, Harti A, Desrochers A: Validated spectral angle mapper algorithm for geological mapping: comparative study between quickbird and landsat-tm. The 20th International Society for Photogrammetry and Remote Sensing Congress, July 2004, Istanbul, Turkey 599–605.

    Google Scholar 

  15. Chassaing R: Digital Signal Processing and Applications with the C6713 and C6416 DSK. John Wiley & Sons, New York, NY, USA; 2005.

    Google Scholar 

  16. Texas Instrument : Datasheet of TMS320C6713B. 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kyoung-Su Park.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Park, KS., Hong, S., Park, P. et al. Spectral Content Characterization for Efficient Image Detection Algorithm Design. EURASIP J. Adv. Signal Process. 2007, 082874 (2007).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI:


  • Information Technology
  • Quantum Information
  • Optimal Number
  • Detection Algorithm
  • Processing Technique