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Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images

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Abstract

Non-Gaussian triplet Markov fields (TMF) model is suitable for dealing with multi-class segmentation of non-stationary and non-Gaussian synthetic aperture radar (SAR) images. Considering the complexity of the model and algorithm, as well as the requirement of real-time, and robust and efficient processing of SAR images, a fast algorithm based on TMF for unsupervised multi-class segmentation of SAR images is proposed in this paper. For the speckle noise in SAR images, numerical characteristic, threshold selection and QuadTree decomposition criterion are researched firstly. With the new method, a SAR image can quickly be mapped into an edge-based pixon-representation, which results in a coarse decomposition in smooth regions, and a fine decomposition in edges. Then by combining TMF model with the pixon-representation of SAR image, a new potential energy function of TMF based on pixon-representation is derived. Finally, the segmentation is finished by Bayesian maximum posterior mode (MPM). The effectiveness of the fast TMF algorithm is demonstrated by applying it to simulated data and real SAR images.

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References

  1. Giordana N, Pieczynski W. Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation. IEEE Trans Pattern Anal Mach Intell, 1997, 19: 465–475

    Article  Google Scholar 

  2. Poggi G, Scarpa G, Zerubia J. Supervised segmentation of remote sensing images based on a tree-structured MRF model. IEEE Trans Geosci Remote Sens, 2005, 43: 1901–1911

    Article  Google Scholar 

  3. Benboudjema D, Tupin F, Pieczynski W, et al. Unsupervised segmentation of SAR images using triplet Markov fields and Fisher noise distributions. In: IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007. 3891–3893

  4. Benboudjema D, Pieczynski W. Multisensor triplet Markov fields and theory of evidence. Image Vision Comput, 2006, 24: 61–69

    Article  Google Scholar 

  5. Lanchantin P, Pieczynski W. Unsupervised segmentation of triplet Markov chains hidden with long-memory noise. Adv Concepts Intell Vision Syst, 2008, 88: 1134–1151

    MATH  Google Scholar 

  6. Blanchet J, Forbes F. Triplet Markov fields for the classification of complex structure data. IEEE Trans Pattern Anal Mach Intell, 2008, 30: 1055–1067

    Article  Google Scholar 

  7. Benboudjema D, Pieczynski W. Unsupervised image segmentation using triplet Markov fields. Comput Vision Image Understand, 2005, 99: 476–498

    Article  Google Scholar 

  8. Benboudjema D, Pieczynski W. Unsupervised statistical segmentation of nonstationary images using triplet Markov fields. IEEE Trans Pattern Anal Mach intell, 2007, 29: 1367–1378

    Article  Google Scholar 

  9. Benboudjema D, Pieczynski W. Segmenting non stationary images with triplet Markov fields. In: International Conference on Image Processing (ICIP2005), Geneva, Italy, 2005. 11–14

  10. Benboudjema D, Pieczynski W. Unsupervised segmentation of non stationary images with non Gaussian correlated noise using triplet Markov fields and the Pearson system. In: Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, Vol. 2, Toulouse, France, 2006

  11. Pieczynski W. Multisensor triplet Markov chains and theory of evidence. Int J Approx Reason, 2007, 45: 1–16

    Article  MATH  MathSciNet  Google Scholar 

  12. Pina R K, Puetter R C. Bayesian image reconstruction: the pixon and optimal image modeling. PASP, 1993, 105: 630–637

    Article  Google Scholar 

  13. Yang F, Jiang T. Pixon-based image segmentation with Markov random fields. IEEE Trans Image Process, 2003, 12: 1552–1559

    Article  Google Scholar 

  14. Lin L, Zhu L, Yang F, et al. A novel pixon-representation for image segmentation based on Markov random field. Image Vision Comput, 2008, 26: 1507–1514

    Article  Google Scholar 

  15. Papila I, Yazgan B. An unsupervised segmentation method based on MPM for SAR images. Geosci Remote Sens Lett, 2005, 2: 55–58

    Article  Google Scholar 

  16. Jedynak B, Zheng H, Daoudi M. Skin detection using pairwise models. Image Vision Comput, 2005, 23: 1122–1130

    Article  Google Scholar 

  17. Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell, 1984, 6: 721–741

    Article  MATH  Google Scholar 

  18. Ibanez M V, Simo A. Parameter estimation in Markov random field image modeling with imperfect observations: A comparative study. Pattern Recognit Lett, 2003, 24: 2377–2389

    Article  MATH  Google Scholar 

  19. Caves R, Quegna S, White R. Quantitative comparison of the perfomance of SAR segmentation algorithms. IEEE Trans Image Process, 1998, 7: 1534–1546

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electronics Engineering, Xidian University, Xi’an, 710071, China

    Yan Wu, Xin Wang, Lu Gan & ChunYan Liu

  2. Shaanxi Bureau of Surveying & Mapping, Xi’an, 710054, China

    Ping Xiao

  3. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China

    Ming Li

Authors
  1. Yan Wu
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  2. Xin Wang
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  3. Ping Xiao
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  4. Lu Gan
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  5. ChunYan Liu
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  6. Ming Li
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Correspondence to Yan Wu.

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Wu, Y., Wang, X., Xiao, P. et al. Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images. Sci. China Inf. Sci. 54, 1524–1533 (2011). https://doi.org/10.1007/s11432-011-4215-x

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  • DOI: https://doi.org/10.1007/s11432-011-4215-x

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