Automatic Extraction and Classification of Vegetation Areas from High Resolution Images in Urban Areas

  • Corina Iovan
  • Didier Boldo
  • Matthieu Cord
  • Mats Erikson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

This paper presents a complete high resolution aerial-images processing workflow to detect and characterize vegetation structures in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. To detect urban vegetation areas, we develop two methods, one using spectral indices and the second one based on a Support Vector Machines (SVM) classifier. Once vegetation areas detected, we differentiate lawns from treed areas by computing a texture operator on the Digital Surface Model (DSM). A robust region growing method based on the DSM is proposed for an accurate delineation of tree crowns. Delineation results are compared to results obtained by a Random Walk region growing technique for tree crown delineation. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward.

Keywords

Support Vector Machine Spectral Index Tree Crown Seed Point Aerial Image 
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.

References

  1. 1.
    Taillandier, F., Vallet, B.: Fitting constrained 3d models in multiple aerial images. In: British Machine Vision Conference, Oxford, U.K. (Aug. 2005)Google Scholar
  2. 2.
    Pinz, A.: Tree isolation and species classification. In: Hill, D.A., Leckie, D.G. (eds.) Proc. of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, British Columbia, Canada, February 1998, pp. 127–139 (1998)Google Scholar
  3. 3.
    Wulder, M.A., White, J.C., Niemann, K.O., Nelson, T.: Comparison of airborne and satellite high spatial resolution data for the identifcation of individual trees with local maxima filtering. International Journal of Remote Sensing 25(11), 2225 (2004)CrossRefGoogle Scholar
  4. 4.
    Gougeon, F.A., Leckie, D.G.: Individual tree crown image analysis - a step towards precision forestry. In: Proc. of the First International Precision Forestry Symposium, Seattle, U.S. (Jun. 2001)Google Scholar
  5. 5.
    Gougeon, F.A.: A system for individual tree crown classification of conifer stands at high spatial resolution. In: Proc. of the 17th Cananadian Symposium on Remote Sensing, Saskatchewan, Canada, June 1995, pp. 635–642 (1995)Google Scholar
  6. 6.
    Erikson, M.: Segmentation and Classification of Individual Tree Crowns. PhD thesis, Swedish University of Agricultural Sciences, Uppsala, Sweden (2004)Google Scholar
  7. 7.
    Brandtberg, T., Walter, F.: Automatic delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications 11(2), 64–73 (1998)CrossRefGoogle Scholar
  8. 8.
    Horvath, P., Jermyn, I.H., Kato, Z., Zerubia, J.: A higher-order active contour model for tree detection. In: Proc. International Conference on Pattern Recognition (ICPR), Hong Kong (August 2006)Google Scholar
  9. 9.
    Pollock, R.: The Automatic Recognition of Individual Trees in Aerial Images of Forests based upon a Synthetic Tree Crown Image Model. PhD thesis, University of BC, Department of Computer Science, Vancouver, Canada (1996)Google Scholar
  10. 10.
    Larsen, M.: Crown modelling to find tree top positions in aerial photographs. In: Proc. of the Third Internationl Airborne Remote Sensing Conference and Exhibition, vol. 2, pp. 428–435 (1997)Google Scholar
  11. 11.
    Perrin, G., Descombes, X., Zerubia, J.: 2d and 3d vegetation resource parameters assessment using marked point processes. In: Proc. International Conference on Pattern Recognition (ICPR), pp. 1–4 (2006)Google Scholar
  12. 12.
    Straub, B.M.: Automatic extraction of trees from aerial images and surface models. In: ISPRS Conference Photogrammetric Image Analysis (PIA), vol. XXXIV, Part 3/W834, Munich, Germany (Sep. 2003)Google Scholar
  13. 13.
    Rottensteiner, F., Trinder, J., Clode, S., Kubik, K.: Using the dempster-shafer method for the fusion of lidar data and multi-spectral images for building detection. Information Fusion 6(4), 283–300 (2005)CrossRefGoogle Scholar
  14. 14.
    Mei, C., Durrieu, S.: Tree crown delineation from digital elevation models and high resolution imagery. In: Proc. of the ISPRS Workshop ’Laser scanners for Forest and Landscape Assement’, vol. 36, Fribourg, Germany (Oct. 2004)Google Scholar
  15. 15.
    Pierrot-Deseilligny, M., Paparoditis, N.: A multiresolution and optimization-based image matching approach: An application to surface reconstruction from spot5-hrs stereo imagery. In: Proc. of the ISPRS Conference Topographic Mapping From Space (With Special Emphasis on Small Satellites), Ankara, Turkey, Feb. 2006, ISPRS (2006)Google Scholar
  16. 16.
    Roy, S., Cox, I.J.: A maximum-flow formulation of the n-camera stereo correspondence problem. In: Proc. of the IEEE International Conference on Computer Vision, Bombay, India, Jan. 1998, pp. 492–499. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  17. 17.
    Gong, P., Pu, R., Biging, G.S., Larrieu, M.R.: Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 41(6), 1355–1362 (2003)CrossRefGoogle Scholar
  18. 18.
    Mathieu, R., Pouget, M., Cervelle, B., Escadafal, R.: Relationships between satellite based radiometric indices simulated using laboratory reflectance data and typic soil colour of an arid environment. Remote Sensing of Environment 66, 17–28 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Corina Iovan
    • 1
    • 2
  • Didier Boldo
    • 1
  • Matthieu Cord
    • 2
  • Mats Erikson
    • 3
  1. 1.Institut Géographique National, Laboratoire MATIS, 2/4 avenue Pasteur, 94165 Saint-Mandé CedexFrance
  2. 2.Université Pierre et Marie Curie, Laboratoire d’Informatique de Paris VI, 8 rue du Capitaine Scott, 75015 ParisFrance
  3. 3.ARIANA Research Group, CNRS/INRIA/UNSA, 2004, route des Lucioles - BP 93, 06902 Sophia Antipolis CedexFrance

Personalised recommendations