Journal of Real-Time Image Processing

, Volume 1, Issue 2, pp 109–121

Sensor band selection for multispectral imaging via average normalized information

Special Issue


The information-rich scene descriptors created by multispectral sensors can act as a bottleneck in further analysis, e.g., real-time scene capturing. Many of the spectral band selection methods treat the two underlying tasks (feature bands selection and redundancy reduction) in isolation. Furthermore, the majority of the work assumes reflectance data. However, the captured surface radiance varies with scene geometry and illumination. We propose a new band selection method, which uses spectral gradient entropy to choose bands that are more stable to such variations. Equally important, our measurement, the average normalized information (ANI) of a set of selected bands, combines feature band selection and band redundancy together. Since feature stability is an important criterion for band selection in ANI, our method favors features whose probability density can be accurately estimated. As a result, our technique selects the most representative feature bands that can be efficiently used in classification. In our experiments, ANI exhibited comparable performance with mutual information on reflectance data but outperformed mutual information when applied on surface radiance data.


Multispectral imaging Band selection Entropy Material classification 


  1. 1.
    Angelopoulou, E: Objective colour from multispectral imaging. In: Proceedings of 6th European Conference in Computer Vision, pp. 359–374 (2000)Google Scholar
  2. 2.
    Angelopoulou, E.: Understanding the color of human skin. SPIE Conference on Human Vision and Electronic Imaging VI. SPIE 4299, 243–251 (2001)Google Scholar
  3. 3.
    Bajcsy, P., Groves, P.: Methodology for hyperspectral band selection. Photogram. Eng. Remote Sens. J. 70, 793–802 (2004)Google Scholar
  4. 4.
    Bassett, E.M., Shen, S.S.: Information Theory-Based Band Selection for Multispectral Systems. Proc. SPIE 3118, 28–35 (1997)Google Scholar
  5. 5.
    Belhumeur, PN., Hespanha, JP., Kriegman, DJ.: Eigenfaces versus Fisherfaces: recognition using class specific linear projection. IEEE Trans. PAMI, 19(7), 711–720 (1997)Google Scholar
  6. 6.
    Chang, CI., Du, Q., Sun, TL., Althouse, LG.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37(6), 2631– 2641 (1999)CrossRefGoogle Scholar
  7. 7.
    Chang, CI., Ren, H., Chiang, SS.: Real-time processing algorithms for target detection and classification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 39, 4 (2001)Google Scholar
  8. 8.
    Chellapa, R., Wilson, C., Sirohey, S.: Human and machine recognition of faces: a survey. Proc. IEEE 85(5), 705–740 (1995)CrossRefGoogle Scholar
  9. 9.
    Du, H., Qi, H., Wang, X., Ramanath, R., Snyder, WE.: Band selection using independent component analysis for hyperspectral image processing. In: Proceedings AIPR workshop, pp. 93–98 (2003)Google Scholar
  10. 10.
    Gat, N.: Imaging spectroscopy using tunable filters: a review. Proc. SPIE 4056, 50–64 (2000)Google Scholar
  11. 11.
    Healey, G., Slater, D.: Invariant recognition in hyperspectral images. In: Proceeding IEEE Conference on Computer Vision and Pattern Recognition, pp. 438–443 (1999)Google Scholar
  12. 12.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)CrossRefGoogle Scholar
  13. 13.
    Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. PAMI 19(2), 153–158 (1997)Google Scholar
  14. 14.
    Koller, D., Sahami, M.: Towards optimal feature selection. In: Proceedings of the 13th International Conference on Machine Learning, pp. 284–292 (1996)Google Scholar
  15. 15.
    Lennon, M., Mercier, G., Mouchot, MC., Hubert-Moy, L.: Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images. In: International Geoscience and Remote Sensing Symposium (IGARSS) (2001)Google Scholar
  16. 16.
    Papoulis, A.: Probability, Random Variables, and Stochastic Process, 3rd edn. McGraw-Hill, New York (1991)Google Scholar
  17. 17.
    Parkkinen, J., Oja, E., Jääskeläinen, T.: Color analysis by learning subspaces and optical processing, In: Proceedings of the International Conference on Neural Networks, San Diego, USA, vol. 2, pp. 421–427 July 24–27 (1988)Google Scholar
  18. 18.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  19. 19.
    Rennich, B.D.: Active multispectral band selection and reflectance measurement system. Master Thesis, Air Force Institute of Technology (1999)Google Scholar
  20. 20.
    Richards, A.: Alien Vision. SPIE Press, Bellingham (2001)Google Scholar
  21. 21.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  22. 22.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. Journal 27:379–423 and pp. 623–656 (1948)Google Scholar
  23. 23.
    Shi, D., Angelopoulou, E.: Dimensionality reduction for multispectral skin data. Institute of Technology Technical Report CS-2004-9 (2004)Google Scholar
  24. 24.
    Slater, D., Healey, G.: Physics-based model acquisition and identification in airborne spectral images. In: International conference on computer vision, pp. 257–262 (2001)Google Scholar
  25. 25.
    Soriano, M., Marszalec, E., Pietikäinen, M.: Color correction of face images under different illuminants by RGB Eigenfaces. In: Proceedings of 2nd audio- and video-based biometric person authentication conference (AVBPA99), Washington DC USA 148–153 (1999)Google Scholar
  26. 26.
    Sotoca, J.M., Pla, F., Klaren, A.C.: Unsupervised band selection for multispectral images using information theory. In: International conference on pattern recognition (2004)Google Scholar
  27. 27.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A global framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  28. 28.
    Turk, M.A., Pentland, A.P.: Face recognition using Eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  29. 29.
    Vidal-Naquet, M., Ullman, S.: Object recognition with informative features and linear classification. In: International Conference on Computer vision, pp. 281–288 (2003)Google Scholar
  30. 30.
    Withagen, PJ., Breejen, E., Franken, EM., de Jong, AN., Winkel, H.: Band Selection From a hyperspectral data-cube for a real-time multispectral 3CCD camera. In: Proceedings of SPIE AeroSense, Algorithms for Multi-Hyper, and Ultraspectral Imagery VIIs, Florida, April 16–20 (2001)Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceStevens Institute of TechnologyHobokenUSA

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