International Journal of Computer Vision

, Volume 88, Issue 1, pp 111–128 | Cite as

Extended Phase Field Higher-Order Active Contour Models for Networks

Its Application to Road Network Extraction from VHR Satellite Images
  • Ting Peng
  • Ian H. Jermyn
  • Véronique Prinet
  • Josiane Zerubia
Article

Abstract

This paper addresses the segmentation from an image of entities that have the form of a ‘network’, i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present new phase field higher-order active contour (HOAC) prior models for network regions, and apply them to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much ‘noise’ in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular.

Keywords

Active contour Phase field Shape prior Parameter analysis Remote sensing Road network extraction 

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References

  1. Amo, M., Martínez, F., & Torre, M. (2006). Road extraction from aerial images using a region competition algorithm. IEEE Transactions on Image Processing, 15(5), 1192–1201. CrossRefGoogle Scholar
  2. Bertozzi, A., Esedoglu, S., & Gillette, A. (2007). Inpainting of binary images using the Cahn-Hilliard equation. IEEE Transactions on Image Processing, 16(1), 285–291. CrossRefMathSciNetGoogle Scholar
  3. Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggs, R., & Geiser, E. (2002). Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision, 50(3), 315–328. MATHCrossRefGoogle Scholar
  4. Cremers, D., Tischhäuser, F., Weickert, J., & Schnörr, C. (2002). Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional. International Journal of Computer Vision, 50(3), 295–313. MATHCrossRefGoogle Scholar
  5. Cremers, D., Osher, S., & Soatto, S. (2006). Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision, 69(3), 335–351. CrossRefGoogle Scholar
  6. Dobrosotskaya, J. A., & Bertozzi, A. L. (2008). A wavelet-Laplace variational technique for image deconvolution and inpainting. IEEE Transactions on Image Processing, 17(5), 657–663. CrossRefMathSciNetGoogle Scholar
  7. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741. MATHCrossRefGoogle Scholar
  8. Geman, D., & Jedynak, B. (1996). An active testing model for tracking roads from satellite images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(1), 1–14. CrossRefGoogle Scholar
  9. Heipke, C., Mayr, H., Wiedemann, C., & Jamet, O. (1997). Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, XXXII, 47–56. Google Scholar
  10. Hu, J., Razdan, A., Femiani, J. C., Cui, M., & Wonka, P. (2007). Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 4144–4157. CrossRefGoogle Scholar
  11. Ising, E. (1925). Beitrag zur theorie des ferromagnetismus. Zeitschrift für Physik, 31, 253–258. CrossRefGoogle Scholar
  12. Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331. CrossRefGoogle Scholar
  13. Lacoste, C., Descombes, X., & Zerubia, J. (2005). Point processes for unsupervised line network extraction in remote sensing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1568–1579. CrossRefGoogle Scholar
  14. Leventon, M. E., Grimson, W. E. L., & Faugeras, O. (2000). Statistical shape influence in geodesic active contours. In Proc. IEEE conference on computer vision and pattern recognition, Hilton Head Island, South Carolina, USA. Google Scholar
  15. Mena, J. B. (2003). State of the art on automatic road extraction for GIS update: A novel classification. Pattern Recognition Letters, 24(16), 3037–3058. CrossRefGoogle Scholar
  16. Merlet, N., & Zerubia, J. (1996). New prospects in line detection by dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4), 426–431. CrossRefGoogle Scholar
  17. Peng, T., Jermyn, I. H., Prinet, V., & Zerubia, J. (2008a). An extended phase field higher-order active contour model for networks and its application to road network extraction from very high resolution satellite image. In Proc. European conference on computer vision, Marseille, France. Google Scholar
  18. Peng, T., Jermyn, I. H., Prinet, V., & Zerubia, J. (2008b). Extraction of main and secondary roads in VHR images using a higher-order phase field model. In Proc. XXI ISPRS congress, commission III, Part A, Beijing, China. Google Scholar
  19. Péteri, R., & Ranchin, T. (2003). Detection and extraction of road networks from high resolution satellite images. In Proc. international conference on image processing, Barcelona, Spain. Google Scholar
  20. Riklin-Raviv, T., Kiryati, N., & Sochen, N. (2007). Prior-based segmentation and shape registration in the presence of perspective distortion. International Journal of Computer Vision, 72(3), 309–328. CrossRefGoogle Scholar
  21. Rochery, M., Jermyn, I. H., & Zerubia, J. (2005). Phase field models and higher-order active contours. In Proc. IEEE international conference on computer vision, Beijing, China. Google Scholar
  22. Rochery, M., Jermyn, I. H., & Zerubia, J. (2006). Higher-order active contours. International Journal of Computer Vision, 69(1), 27–42. CrossRefGoogle Scholar
  23. Rousson, M., & Paragios, N. (2007). Prior knowledge, level set representations & visual grouping. International Journal of Computer Vision, 76(3), 1573–1405. Google Scholar
  24. Srivastava, A., Joshi, S., Mio, W., & Liu, X. (2003). Statistical shape analysis: Clustering, learning, and testing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4), 590–602. CrossRefGoogle Scholar
  25. Thom, R. (1975). Structural stability and morphogenesis. Elmsford: Benjamin/Addison-Wesley. MATHGoogle Scholar
  26. Wang, R., & Zhang, Y. (2003). Extraction of urban road network using Quickbird pan-sharpened multispectral and panchromatic imagery by performing edge-aided post-classification. In Proc. international society for photogrammetry and remote sensing (ISPRS), Quebec City, Canada. Google Scholar
  27. Yu, Z., Prinet, V., Pan, C., & Chen, P. (2004). A novel two-steps strategy for automatic GIS-image registration. In Proc. international conference on image processing, Singapore. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Ting Peng
    • 1
    • 2
  • Ian H. Jermyn
    • 2
  • Véronique Prinet
    • 1
  • Josiane Zerubia
    • 2
  1. 1.LIAMA & NLPR, CASIABeijingChina
  2. 2.Project-Team Ariana, INRIASophia AntipolisFrance

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