A Morphological Neural Network Approach for Vehicle Detection from High Resolution Satellite Imagery

  • Hong Zheng
  • Li Pan
  • Li Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


This paper introduces a morphological neural network approach to extract vehicle targets from high resolution panchromatic satellite imagery. In the approach, the morphological shared-weight neural network (MSNN) is used to classify image pixels on roads into vehicle targets and non-vehicle targets, and a morphological preprocessing algorithm is developed to identify candidate vehicle pixels. Experiments on 0.6 meter resolution QuickBird panchromatic data are reported in this paper. The experimental results show that the MSNN has a good detection performance.


Road Segment Road Surface Vehicle Detection Vehicle Target Gray Level Histogram 
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.


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  1. 1.
    Ruskone, R., Guigues, L., Airault, S., Jamet, O.: Vehicle Detection on Aerial Images: A Structural Approach. In: Proceedings of International Conference On Pattern Recognition, Vienna, Austria, pp. 900–904 (1996)Google Scholar
  2. 2.
    Zhao, T., Nevatia, R.: Car Detection in Low Resolution Aerial Image. In: Proceedings of International Conference on Computer Vision, Vancouver, Canada, pp. 710–717 (2001)Google Scholar
  3. 3.
    Schlosser, C., Reitberger, J., Hinz, S.: Automatic Car Detection in High Resolution Urban Scenes Based on An Adaptive 3D-model. In: Proceedings of the 2nd GRSS/ISPRS Joint Workshop on Data Fusion and Remote Sensing over Urban Area, Berlin, Germany, pp. 167–170 (2003)Google Scholar
  4. 4.
    Stilla, U., Michaelsen, E., Soergel, U., Hinz, S., Ender, H.J.: Airborne Monitoring of Vehicle Activity in Urban Areas. In: Altan, M.O. (ed.) International Archives of Photogrammetry and Remote Sensing, 35(B3), pp. 973–979 (2004)Google Scholar
  5. 5.
    Sharma, G.: Vehicle Detection and Classification in 1-m Resolution Imagery. Ohio State University, Master of Science thesis (2002)Google Scholar
  6. 6.
    Gerhardinger, A., Ehrlich, D., Pesaresi, M.: Vehicles Detection from Very High Resolution Satellite Imagery. In: Stilla, U., Rottensteiner, F., Hinz, S. (eds.) International Archives of Photogrammetry and Remote Sensing, vol. XXXVI, part 3/W24, pp. 83–88 (2005)Google Scholar
  7. 7.
    Won, Y.: Nonlinear Correlation Filter and Morphology Neural Networks for Image Pattern and Automatic Target Recognition. Ph.D. Thesis, University of Missouri, Columbia, Miss (1995)Google Scholar
  8. 8.
    Won, Y., Gader, P.D., Coffield, P.: Morphological Shared-Weight Networks with Applications to Automatic Target Recognition. IEEE Trans. on Neural Networks 8, 1195–1203 (1997)CrossRefGoogle Scholar
  9. 9.
    Khabou, M.A., Gader, P.D., Keller, J.M.: LADAR Target Detection using Morphological Shared-weight Neural Networks. Machine Vision and Applications 11, 300–305 (2000)CrossRefGoogle Scholar
  10. 10.
    Serra, J.: Image Analysis and Mathematical Morphology, vol. 2. Academic Press, New York (1988)Google Scholar
  11. 11.
    Gader, P.D., Won, Y., Khabou, M.: Image Algebra Networks for Pattern Classification. In: Proceedings of SPIE Conference on Image Algebra and Morphological Image Processing, vol. 2300, pp. 157–168 (1994)Google Scholar
  12. 12.
    Gader, P.D., Miramonti, J.R., Won, Y., Coffield, P.: Segmentation Free Shared-Weight Networks for Automatic Vehicle Detection. Neural Networks 8, 1457–1473 (1995)CrossRefGoogle Scholar
  13. 13.
    Ostu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE Transactions on System, Management, Cybernet. 9, 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hong Zheng
    • 1
  • Li Pan
    • 2
  • Li Li
    • 1
  1. 1.Research Center for Intelligent Image Processing and Analysis, School of Electronic InformationWuhan UniversityWuhan, HubeiChina
  2. 2.School of Remote Sensing Information& EngineeringWuhan UniversityWuhan, HubeiChina

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