A Spatial Regularity Constrained Active Contour Model for PolSAR Image Segmentation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


A variational active contour model based on the statistical model and local information of PolSAR images is proposed for PolSAR image segmentation. The energy functional of the proposed model consists of two parts: the likelihood data term, and the regularization term. We introduce a spatial regularization term which imposes a regional homogeneity effect on the segmentation results. The contours are evolved by minimizing the functional using fuzzy region competition method. With the above two modification, the proposed method can lead to more accurate and efficient PolSAR image segmentation algorithm than method based on standard level set method. Experimental results on both synthetic and real PolSAR images are shown. Performance evaluation and comparison with another method are also given.


Polarimetric aperture radar Image segmentation Variational method Spatial regularization 


  1. 1.
    Beaulieu J-M, Touzi R (2004) Segmentation of textured polarimetric SAR scenes by likelihood approximation. IEEE Trans Geosci Rem Sens 42(10):2063–2072CrossRefGoogle Scholar
  2. 2.
    Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefGoogle Scholar
  3. 3.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Imag Process 10(2):266–277CrossRefMATHGoogle Scholar
  4. 4.
    Ayed IB, Mitiche A, Belhadj Z (2005) Multiregion level-set partitioning of synthetic aperture radar images. IEEE Trans Pattern Anal Mach Intell 27(5):793–800CrossRefGoogle Scholar
  5. 5.
    Silverira M, Heleno S (2009) Separation between water and land in SAR images using region-based level sets. IEEE Geosci Rem Sens Lett 6(3):471–475CrossRefGoogle Scholar
  6. 6.
    Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175CrossRefGoogle Scholar
  7. 7.
    Shuai NY, Sun H, Xu G (2008) SAR image segmentation based on level set with stationary global minimum. IEEE Geosci Rem Sens Lett 5(4):644–648CrossRefGoogle Scholar
  8. 8.
    Frery AC, J-berlles J, Gambini J, Mejail ME (2010) Polarimetric SAR image segmentation with B-Splines and a new statistical model. Multidim Syst Sign Process 21:319–324CrossRefMATHGoogle Scholar
  9. 9.
    Ayed IB, Mitiche A, Belhadj Z (2006) Polarimetric image segmentation via maximum- likelihood approximation and efficient multiphase level-sets. IEEE Trans Pattern Anal Mach Intell 28(9):1493–1500CrossRefGoogle Scholar
  10. 10.
    Chen Y, Tagare H, Thiruvenkadam S et al (2002) Using shape priors in geometric active contours in a variational framework. Int J Comput Vis 50(3):315–328CrossRefMATHGoogle Scholar
  11. 11.
    Cremers D, Sochen N, Schnorr C (2006) A multiphase dynamic labeling model for variational recognition-driven image segmentation. Int J Comput Vis 66(1):67–81CrossRefGoogle Scholar
  12. 12.
    Li C, Xu C, Gui C (2005) Level set evolution without re-initialization: a new variational formulation. In: Proc IEEE CVPR, San Diego, CA, 2005, pp 430–436Google Scholar
  13. 13.
    Bresson X, Esedoglu S, Vandergheynst P et al (2007) Fast global minimum of the active contour/snake model. J Math Imag Vis 28(1):151–167CrossRefMathSciNetGoogle Scholar
  14. 14.
    Li F, Ng MK, Zeng TY, Shen C (2010) A multiphase image segmentation method based on fuzzy region competition. SIAM J Imag Sci 3(3):277–299CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Chambolle A (2004) An algorithm for total variation minimization and application. J Math Imag Vis 20(1–2):89–97MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations