Olive Trees Detection in Very High Resolution Images

  • Juan Moreno-Garcia
  • Luis Jimenez Linares
  • Luis Rodriguez-Benitez
  • Cayetano Solana-Cipres
Part of the Communications in Computer and Information Science book series (CCIS, volume 81)


This paper focuses on the detection of olive trees in Very High Resolution images. The presented methodology makes use of machine learning to solve the problem. More concretely, we use the K-Means clustering algorithm to detect the olive trees. K-Means is frequently used in image segmentation obtaining good results. It is an automatic algorithm that obtains the different clusters in a quick way. In this first approach the tests done show encouraging results detecting all trees in the example images.


European Union High Resolution Image Olive Tree Tree Crown Common Agricultural Policy 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arthur, D., Vassilvitskii, S.: How Slow is the K-Means Method? In: Proceedings of the 2006 Symposium on Computational Geometry (SoCG), pp. 144–153 (2006)Google Scholar
  2. 2.
    Brandberg, T., Walter, F.: Automated Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images by Multi-Scale Analysis. Machine Vision and Applications 11, 64–73 (1998)CrossRefGoogle Scholar
  3. 3.
    Gougeon, F.: A crown following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing 3(21), 274–284 (1995)Google Scholar
  4. 4.
    European Comission, Joint Research Center, (last visit January 25, 2010)
  5. 5.
    Lloyd, S.P.: Least square quantization in PCM. Bell Telephone Laboratories Paper (1982); Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (1957)Google Scholar
  6. 6.
    Karantzalos, K., Argialas, D.: Towards the automatic olive trees extraction from aerial and satellite imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35(5), 1173–1177 (2004)Google Scholar
  7. 7.
    Kay, S., Leo, P., Peedel, S., Giordino, G.: Computer-assisted recognition of olive trees in digital imagery. In: Proceedings of International Society for Photogrammetry and Remote Sensing Conference, pp. 6–16 (1998)Google Scholar
  8. 8.
    MacKay, D.: An Example Inference Task: Clustering. In: Information Theory, Inference and Learning Algorithms, ch. 20, pp. 284–292. Cambridge University Press, Cambridge (2003)Google Scholar
  9. 9.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press (1967)Google Scholar
  10. 10.
    Masson, J.: Use of Very High Resolution Airborne and Spaceborne Imagery: a Key Role in the Management of Olive, Nuts and Vineyard Schemes in the Frame of the Common Agricultural Policy of the European Union. In: Proceedings of the Information and Technology for Sustainable Fruit and Vegetable Production (FRUTIC 2005), pp. 709–718 (2005)Google Scholar
  11. 11.
    Bagli, S.: Olicount v2, Technical documentation, Joint Research Centre IPSC/G03/P/SKA/ska D (5217) (2005)Google Scholar
  12. 12.
    Pollock, R.J.: A model-based approach to automatically locating tree crowns in high spatial resolution images. In: Desachy (ed.) Image and Signal Processing for Remote Sensing. SPIE, vol. 2315, 526–537 (1994)Google Scholar
  13. 13.
    Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Weka Software, (last visit January 25, 2010)
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan Moreno-Garcia
    • 1
  • Luis Jimenez Linares
    • 2
  • Luis Rodriguez-Benitez
    • 2
  • Cayetano Solana-Cipres
    • 2
  1. 1.Escuela Universitaria de Ingenieria Tecnica Industrial, Universidad de Castilla-La ManchaToledoSpain
  2. 2.Escuela Superior de InformaticaUniversidad de Castilla-La ManchaCiudad RealSpain

Personalised recommendations