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2012-04-03
Variational SAR Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method
By
Jilan Feng,

    Dr. Jilan Feng

    School of Electronic Engineering,

    University of Electronic Science and Technology of China

    China

    Homepage

Zongjie Cao,

    Dr. Zongjie Cao

    School of Electronic Engineering,

    University of Electronic Science and Technology of China

    China

    Homepage

Yiming Pi

    Dr. Yiming Pi

    School of Electronic Engineering

    University of Electronic Science and Technology of China (UESTC)

    China

    Homepage

Progress In Electromagnetics Research B, Vol. 39, 373-392, 2012
doi:10.2528/PIERB12011212 | Google Scholar

Abstract
This paper present a fast algorithm for synthetic aperture radar (SAR) image segmentation based on the augmented Lagrangian method (ALM). The proposed approach considers the segmentation of SAR images as an energy minimization problem in a variational framework. The energy functional is formulated based on the statistical characteristic of SAR images. The total variation regularization is used to impose the smoothness constraint of the segmentation result. To solve the optimization problem efficiently, the energy functional is firstly modified to be convex and differentiable by using convex relaxing and variable splitting techniques, and then the constrained optimization problem is converted to an unconstrained one by using the ALM. Finally the energy is minimized with an iterative minimization algorithm. The effectiveness of the proposed algorithm is validated by experiments on both synthetic and real SAR images.
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Citation
Jilan Feng, Zongjie Cao, and Yiming Pi, "Variational SAR Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method," Progress In Electromagnetics Research B, Vol. 39, 373-392, 2012.
doi:10.2528/PIERB12011212
References

1. Oliver, C. and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, 1998.

2. Mishra, P., D. Singh, and Y. Yamaguchi, "Land cove classification of PALSAR images by knowledge based decision tree classifier and supervised classifiers based on SAR observation," Progress In Electromagnetics Research B, Vol. 30, 47-70, 2011.        Google Scholar

3. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011.        Google Scholar

4. Jin, Y.-Q., "Polarimetric scattering modeling and information retrieval of SAR remote sensing --- A review of FDU work," Progress In Electromagnetics Research, Vol. 104, 333-384, 2010.
doi:10.2528/PIER10020101        Google Scholar

5. Caselles, V., R. Kimmel, and G. Sapiro, "Geodesic active contours," Int. J. Computer Vision, Vol. 22, No. 1, 61-79, 1997.
doi:10.1023/A:1007979827043        Google Scholar

6. Zhu, S. C. and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 18, No. 9, 884-900, Sept. 1996.
doi:10.1109/34.537343        Google Scholar

7. Chan, T. and L. Vese, "Active contour without edges," IEEE Trans. Image Process., Vol. 10, No. 2, 266-277, 2001.
doi:10.1109/83.902291        Google Scholar

8. Paragios, N. and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," Int. J. Computer Vision, Vol. 46, No. 3, 223-247, 2002.
doi:10.1023/A:1014080923068        Google Scholar

9. Cremers, D., M. Rousson, and R. Deriche, "A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape," Int. J. Computer Vision, Vol. 72, No. 2, 195-215, 2007.
doi:10.1007/s11263-006-8711-1        Google Scholar

10. Ayed, I. B., A. Mitiche, and Z. Belhadj, "Multiregion level-set partitioning of synthetic aperture radar images," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 5, 793-800, 2005.
doi:10.1109/TPAMI.2005.106        Google Scholar

11. Shuai, Y., H. Sun, and G. Xu, "SAR image segmentation based on level set with stationary global minimum," IEEE Geosci. Remote Sens. Lett., Vol. 5, No. 4, 644-648, 2008.
doi:10.1109/LGRS.2008.2001768        Google Scholar

12. Ayed, I. B., N. Hennane, and A. Mitiche, "Unsupervised variational image segmentation classification using a Weibull observation model," IEEE Trans. Image Process., Vol. 15, No. 11, 3431-3439, 2006.
doi:10.1109/TIP.2006.881961        Google Scholar

13. Silveira, M. and S. Heleno, "Separation between water and land in SAR image using region based level set," IEEE Trans. Geoscience and Remote Sensing, Vol. 6, No. 3, 471-475, 2009.
doi:10.1109/LGRS.2009.2017283        Google Scholar

14. Frery, A. C., H. J. Muller, C. C. F. Yanasse, and S. J. S. Sant'Anna, "A model for extremely heterogeneous clutter," IEEE Trans. Geoscience and Remote Sensing, Vol. 35, No. 3, 648-659, 1997.
doi:10.1109/36.581981        Google Scholar

15. Frery, A. C. and F. Yanasse, "Alternative distributions for the multiplicative model in SAR images," International Geoscience and Remote Sensing Symposium, Vol. 1, 169-171, 1995.        Google Scholar

16. Tison, C., J.-M. Nicolas, F. Tupin, and H. Maitre, "A new statistical model for Markovian classification of urban areas in high-resolution SAR images," IEEE Trans. Geoscience and Remote Sensing, Vol. 42, No. 10, 2046-2057, 2004.
doi:10.1109/TGRS.2004.834630        Google Scholar

17. Feng, J., Z. Cao, and Y. P, "A G0 statistical model based level set approach for SAR image segmentation," Proc. EUSAR, 841-844, 2010.        Google Scholar

18. Malladi, R., J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 17, No. 2, 158-175, 1995.
doi:10.1109/34.368173        Google Scholar

19. Li, C., C. Xu, C. Gui, and M. D. Fox, "Level set evolution without re-initialization: A new variational formulation," Proc. CVPR, Vol. 1, 430-436, 2005.        Google Scholar

20. Goldenberg, R., R. Kimmel, E. Rivlin, and M. Rudzsky, "Fast geodesic active contours," IEEE Trans. Image Process., Vol. 10, No. 10, 1467-1475, 2001.
doi:10.1109/83.951533        Google Scholar

21. Shi, Y. and W. C. Karl, "A real-time algorithm for the approximation of level-set-based curve evolution," IEEE Trans. Image Process., Vol. 17, No. 5, 645-656, 2008.
doi:10.1109/TIP.2008.920737        Google Scholar

22. Chan, T. F., S. Esedoglu, and M. Nikolova, "Algorithms for finding global minimizers of image segmentation and denoising models," SIAM J. Appl. Math, Vol. 66, No. 5, 1632-1648, 2006.
doi:10.1137/040615286        Google Scholar

23. Bresson, X., S. Esedoglu, P. Vandergheynst, et al. "Fast global minimization of the active contour/snake model," J. Math. Imaging. Vision., Vol. 28, No. 2, 151-167, 2007.
doi:10.1007/s10851-007-0002-0        Google Scholar

24. Wu, C. and X. C. Tai, "Augmented lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models," SIAM J. Imaging Sci., Vol. 3, No. 3, 300-339, 2010.
doi:10.1137/090767558        Google Scholar

25. Afonso, M. V., J. M. Bioucas-Dias, and M. A. T. Figueiredo, "An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Trans. Image Process., Vol. 20, No. 3, 681-695, 2011.
doi:10.1109/TIP.2010.2076294        Google Scholar

26. Chambolle, A., "An algorithm for total variation minimization and application," J. Math. Imaging. Vision., Vol. 20, No. 1--2, 89-97, 2004.        Google Scholar

27. Rudin, L., S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, Vol. 60, 259-268, 1992.
doi:10.1016/0167-2789(92)90242-F        Google Scholar

28. Beck, A. and M. Teboulle, "Fast gradient-based algorithms for constrained total variation image denosing and deblurring problems," IEEE Trans. Image Process., Vol. 18, No. 11, 2419-2434, 2009.
doi:10.1109/TIP.2009.2028250        Google Scholar

29. Nocedal, J. and S. J. Wright, Numerical Optimization, 2nd Ed., Springer-Verlag, 2006.

30. Powell, M., "A method for nonlinear constraints in minimization problems," Optimization, 283-298, R. Fletcher Edition, Academic, New York, 1969.

31. Bertsekas, D. P., "Multiplier methods: A survey," Automatica, Vol. 12, 133-145, 1976.
doi:10.1016/0005-1098(76)90077-7        Google Scholar

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