A Clustering Based Method for Edge Detection in Hyperspectral Images

  • V. C. Dinh
  • Raimund Leitner
  • Pavel Paclik
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


Edge detection in hyperspectral images is an intrinsically difficult problem as the gray value intensity images related to single spectral bands may show different edges. The few existing approaches are either based on a straight forward combining of these individual edge images, or on finding the outliers in a region segmentation. As an alternative, we propose a clustering of all image pixels in a feature space constructed by the spatial gradients in the spectral bands. An initial comparative study shows the differences and properties of these approaches and makes clear that the proposal has interesting properties that should be studied further.


Edge Detection Hyperspectral Image Spectral Space Edge Detection Method Cluster Base Method 
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.


  1. 1.
    Koschan, A., Abidi, M.: Detection and classification of edges in color images. Signal Processing Magazine, Special Issue on Color Image Processing 22, 67–73 (2005)Google Scholar
  2. 2.
    Robinson, G.: Color edge detection. Optical Engineering, 479–484 (1977)Google Scholar
  3. 3.
    Hedley, M., Yan, H.: Segmentation of color images using spatial and color space information. Journal of Electronic Imaging 1, 374–380 (1992)CrossRefGoogle Scholar
  4. 4.
    Di Zenzo, S.: A note on the gradient of a multi-image. Computer Vision, Graphics, and Image Processing, 116–125 (1986)Google Scholar
  5. 5.
    Trahanias, P., Venetsanopoulos, A.: Color edge detection using vector statistics. IEEE Transactions on Image Processing 2, 259–264 (1993)CrossRefGoogle Scholar
  6. 6.
    Evans, A., Liu, X.: A morphological gradient approach to color edge detection. IEEE Transactions on Image Processing 15(6), 1454–1463 (2006)CrossRefGoogle Scholar
  7. 7.
    Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532–550 (1987)CrossRefGoogle Scholar
  8. 8.
    Barnett, V.: The ordering of multivariate data. J. Royal Statist., 318–343 (1976)Google Scholar
  9. 9.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 679–698 (1986)Google Scholar
  10. 10.
    Field, D.: Relations between the statistics and natural images and the responses properties of cortical cells. Journal of Optical Society of America A(4), 2379–2394 (1987)CrossRefGoogle Scholar
  11. 11.
    Zhu, S.C., Mumford, D.: Prior learning and gibbs reaction-diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(11), 1236–1250 (1997)CrossRefGoogle Scholar
  12. 12.
    Konishi, S., Yuille, A.L., Coughlan, J.M., Zhu, S.C.: Statistical edge detection: Learning and evaluating edge cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(1), 57–74 (2003)CrossRefGoogle Scholar
  13. 13.
    Huntsberger, T., Descalzi, M.: Color edge detection. Pattern Recognition Letter, 205–209 (1985)Google Scholar
  14. 14.
    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of Royal Society of London, 187–217 (1980)Google Scholar
  15. 15.
    Paclik, P., Duin, R.P.W., van Kempen, G.M.P., Kohlus, R.: Segmentation of multi-spectral images using the combined classifier approach. Journal of Image and Vision Computing 21, 473–482 (2005)CrossRefGoogle Scholar
  16. 16.
    Landgrebe, D.: Signal theory methods in multispectral remote sensing. John Wiley and Sons, Chichester (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • V. C. Dinh
    • 1
    • 2
  • Raimund Leitner
    • 2
  • Pavel Paclik
    • 3
  • Robert P. W. Duin
    • 1
  1. 1.ICT GroupDelft University of TechnologyDelftThe Netherlands
  2. 2.Carinthian Tech Research AGVillachAustria
  3. 3.PR Sys DesignDelftThe Netherlands

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