Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Sharma, G.: Vehicle Detection and Classification in 1-m Resolution Imagery. Ohio State University, Master of Science thesis (2002)
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)
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)
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)
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)
Serra, J.: Image Analysis and Mathematical Morphology, vol. 2. Academic Press, New York (1988)
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)
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)
Ostu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE Transactions on System, Management, Cybernet. 9, 62–66 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, H., Pan, L., Li, L. (2006). A Morphological Neural Network Approach for Vehicle Detection from High Resolution Satellite Imagery. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_11
Download citation
DOI: https://doi.org/10.1007/11893257_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)