A Coastline Detection Method Based on Level Set

  • Qian Wang
  • Ke Lu
  • Fuqing Duan
  • Ning He
  • Lei Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)


This paper proposes a level set based coastline detection method by using the template initialization and local energy minimization. It can complete the sea-land boundary detection in infrared channel image. This method is an improvement on the traditional level set algorithm by using the information of GSHHS to optimize the initialization procedure, which can reduce the number of iterations and numerical errors. Moreover, this method optimizes regional energy functional, and can achieve the rapid coastline detection. Experiments on the IR image of FY-2 satellite show that the method has fast speed and high accuracy.


Edge detection level set method IR image processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yang, L., Yang, Z.: The Automated Landmark Navigation of the Polar Meteorological Satellite. J. of Applied Meteorological Science 20(3), 329–336 (2009) (in Chinese) Google Scholar
  2. 2.
    Wang, D., Hou, Y., Peng, J.: The partial differential equations method in image processing. Science Press, Beijing (2010)Google Scholar
  3. 3.
    Caselles, V., Carte, F., Coil, T., et al.: A geometric model for active contours. Numerische Mathematik 66, 1–31 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Kimmel, R.: Fast edge integration. In: Osher, S., Paragios, N. (eds.) Geometric Level Set Methods in Imaging, Vision, and Graphics, pp. 59–78. Springer (2003)Google Scholar
  5. 5.
    Wang, L., Li, C., Sun, Q., et al.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Computerized Medical Imaging and Graphics 33, 520–531 (2009)CrossRefGoogle Scholar
  6. 6.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Li, C., Xu, C., Gui, C., et al.: Level set Evolution without re-initialization:A new variational formulation. In: Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 430–436 (2005)Google Scholar
  8. 8.
    Lee, S.H., Seo, J.: Level set-based bimodal segmentation with stationary global minimum. IEEE Transactions on Image Processing 15(9), 2843–2852 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Tsai, A., Yezzi, A., Wiilsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing 10(8), 169–1186 (2001)CrossRefGoogle Scholar
  10. 10.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah Model. Int. Journal of Computer Vision 50(3), 271–293 (2002)zbMATHCrossRefGoogle Scholar
  11. 11.
    Chen, S., Sochen, N.A., Zeevi, Y.: Integrated active contours for texture segmentation. IEEE Trans. on Image Processing 15(6), 1633–1646 (2006)CrossRefGoogle Scholar
  12. 12.
    Brox, T., Cremers, D.: On the Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 203–213. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Sum, K.W., Cheung, P.Y.S.: Vessel extraction under non-uniform illumination: a level set approach. IEEE Transactions on Biomedical Engineering 55(1), 358–360 (2008)CrossRefGoogle Scholar
  14. 14.
    Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Transactions on Image Processing 17(11), 2029–2039 (2008)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li, C., Kao, C.-Y., Gore, J.C., Ding, Z.: Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transactions on Image Processing 17(10), 1940–1949 (2008)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Li, C., Xu, C., Gui, C., et al.: Distance Regularized Level Set Evolution and Its Application to Image Segmentation. IEEE Transactions on Image Processing 19(12), 3243–3254 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qian Wang
    • 1
  • Ke Lu
    • 1
  • Fuqing Duan
    • 2
  • Ning He
    • 3
  • Lei Yang
    • 4
  1. 1.College of Engineering and Information TechnologyGraduate University of Chinese Academy of SciencesBeijingChina
  2. 2.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  3. 3.School of InformationBeijing Union UniversityBeijingChina
  4. 4.National Satellite Meteorological CenterBeijingChina

Personalised recommendations