Advertisement

Particle Swarm Optimization for Road Extraction in SAR Images

  • Ge Xu
  • Hong Sun
  • Wen Yang
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)

Abstract

The paper proposes a new method for road extraction in SAR images. We regard that the road in SAR images can be represented by the Bspline curve. Firstly, we manually select the road’s extremities. Secondly, we calculate the each pixels’s road membership value using local road detector in the original SAR images. Thirdly, with particle swarm optimization that is one of the most powerful methods for optimization problem we obtain the optimal B-spline control points from the result of road detection. Finally, according to the optimal B-spline control points, we obtain the B-spline curve that is the result of road extraction. The experimental result shows that the method in the paper can accurately extract the road.

Keywords

Particle Swarm Optimization Synthetic Aperture Radar Synthetic Aperture Radar Image Particle Swarm Optimization Method Particle Swarm Optimization Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hellwich. O., Mayer. H., Winkler. G.: Detection of Lines in Synthetic Aperture Radar(SAR)Scenes[A], Proceedings of International Archives of Photogrammetry RemoteSensing[C], Vienna, Austria (1996) 312–320Google Scholar
  2. 2.
    Samadani, R., Vesecky, J.F.:Finding Curvilinear Features in Speckled Images[J], IEEE Transactions on Geoscience and Remote Sensing,(1990) 669–673Google Scholar
  3. 3.
    G eman, D., Jedynak, B.: An Active Testing Model for Tracking Roads in SatelliteImages, IEEETrans. on PAMI (1996) 1–14Google Scholar
  4. 4.
    T upin, F., Maître, H., Mangin, J.F., Nicolas, J.M., et Pechersky, E.: Detection of LinearFeatures in SAR Images: Application to Road Network Extraction, IEEE Trans. Geosci.and Remote Sensing vol. 36, no. 2, March (1998)Google Scholar
  5. 5.
    Fabio Dell’Acqua, Paolo Gamba.: Detection of Urban Structures in SAR Images by Robust Fuzzy Clustering Algorithms: The Example of Street Tracking[J], IEEE Transactions on Geoscience and Remote Sensing,(2001)Google Scholar
  6. 6.
    Gruer, A., Li, H H.: Semiautomatic Linear Feature Extraction with Dynamic Programming and LSB-snakes[J], Photogrammetric Engineering and Remote Sensing (1997)Google Scholar
  7. 7.
    Touzi, R.,.Lopes Bousquet A, P.: A Statistical and Geometrical Edge Detector for SAR Images[J].IEEE Transactions Geoscicece and Remote Sensing,(1988) 764–773Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. Proc. IEEE int’l conf. On neural network Vol. IV,. 1942–1948. IEEE service center, Piscataway, NJ (1995)Google Scholar
  9. 9.
    Xu, Wenbo Sun, Jun.: Adaptive Parameter Selection of Quantum-behaved Particle Swarm Optimization on Global Level, Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005. Proceedings (2005) 420–428Google Scholar
  10. 10.
    Kim, Dong Hwa.: Park III, Jin Intelligent PID Controller Tuning of AVR System Using GA and PSO Advances in Intelligent Computing: International Conference onIntelligent Computing, ICIC 2005. Proceedings (2005) 366–375Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ge Xu
    • 1
  • Hong Sun
    • 1
  • Wen Yang
    • 1
  1. 1.Signal Processing LaboratoryWuhan UniversityWuhanChina

Personalised recommendations