access icon free SAR image segmentation algorithm based on Contourlet domain AFMRF model

Combining the advantage of Contourlet transform with adaptive fuzzy Markov random field (MRF) model, a novel segmentation algorithm is proposed in this study to achieve precise and continuous division for synthetic aperture radar (SAR) image. The classical MRF model is modified to obtain the edge and process adaptive label factor. The adaptive fuzzy MRF (AFMRF) model is proposed to equilibrate the smooth regions segmentation with texture regions segmentation. At the same time, Contourlet domain hidden Markov tree (HMT) model is introduced to perform multi-scale directional filtering and intra-scale training on the coefficients of SAR images to achieve precise texture segmentation at each scale. Finally, the AFMRF model is integrated into inter-scale and intra-scale HMT training results and the segmented image can be obtained. To verify the validity of the proposed algorithm, several SAR images are experimented and compared with the state-of-the-art algorithms. The experimental results and analysis show that the proposed algorithm can achieve better results on noise suppression, smoothness of target regions, precise and continuous segmentation of fuzzy texture.

Inspec keywords: image texture; fuzzy set theory; image segmentation; trees (mathematics); synthetic aperture radar; hidden Markov models; radar imaging

Other keywords: noise suppression; continuous segmentation; contourlet domain hidden Markov tree model; SAR image segmentation algorithm; fuzzy texture; precise texture segmentation; contourlet domain AFM model; multiscale directional filtering; contourlet domain HMT model; intrascale training; adaptive fuzzy Markov random field model; smooth region segmentation; adaptive fuzzy MRF model; texture region segmentation; target region smoothness

Subjects: Optical, image and video signal processing; Markov processes; Radar equipment, systems and applications; Combinatorial mathematics

References

    1. 1)
      • 1. Zhang, X.R., Jiao, L.C., Liu, F., et al: ‘Spectral clustering ensemble applied to SAR image segmentation’, IEEE Trans. Geosci. Remote Sens., 2008, 46, (7), pp. 21262136.
    2. 2)
      • 18. Perciano, T., Tupin, F., Hirata, R.: ‘A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data’, Int. J. Remote Sens., 2014, 37, (16), pp. 35843610.
    3. 3)
      • 4. Yu, H., Jiao, L.C., Liu, F.: ‘Context based unsupervised hierarchical iterative algorithm for SAR segmentation’, Acta Autom. Sin., 2014, 40, (1), pp. 100116.
    4. 4)
      • 10. He, F.Y., Tian, Z., Fu, H.J., et al: ‘Efficient segmentation of SAR images using Markov random field models with edge penalties and adaptive weighting parameter’, Acta Opt. Sin., 2013, 33, (8), pp. 93100.
    5. 5)
      • 5. Pilar, J.A., Manuel, R.Z., David de la, M.M.: ‘Spatial-range mean-shift filtering and segmentation applied to SAR images’, IEEE Trans. Instrum. Meas., 2011, 60, (2), pp. 584597.
    6. 6)
      • 6. He, C., Deng, J.B., Xu, L.Y., et al: ‘A novel over-segmentation method for polarimetric SAR images classification’. Geoscience & Remote Sensing Symp., 2012, pp. 42994302.
    7. 7)
      • 2. Yang, X.Z., Ye, M., Wu, K.W., et al: ‘Speckle reduction for PolSAR image based on structure preserving bilateral filtering’, J. Electron. Inf. Technol., 2015, 37, (2), pp. 268275.
    8. 8)
      • 17. Xiao, Y.H., Xi, Z.H., Dong, G.H., et al: ‘Image segmentation based on Contourlet transform’, J. Harbin Univ. Sci. Technol., 2011, 16, (5), pp. 101105.
    9. 9)
      • 19. Li, H., Gong, M.G., Wang, Q., et al: ‘A multi-objective fuzzy clustering method for change detection in SAR images’, Appl. Soft Comput., 2016, 46, pp. 767777.
    10. 10)
      • 13. Subudhi, B.N., Ghosh, S., Cho, S., et al: ‘Integration of fuzzy Markov random field and local information for separation of moving objects and shadows’, Inf. Sci., 2016, 331, (7), pp. 1531.
    11. 11)
      • 16. Guo, L.: ‘Research on multi-scale image segmentation algorithm based on Contourlet transform’ (Harbin Engineering University, Harbin, 2010).
    12. 12)
      • 9. Mignotte, M.: ‘A non-stationary MRF model for image segmentation from a soft boundary map’, Pattern Anal. Appl., 2014, 17, (1), pp. 129139.
    13. 13)
      • 14. Duan, Y.P., Liu, F., Jiao, L.C.: ‘SAR image segmentation based on convolutional-wavelet neural network and Markov random field’, Pattern Recognit., 2017, 64, (12), pp. 255267.
    14. 14)
      • 12. Wang, F., Wu, Y., Fan, J.W., et al: ‘Synthetic aperture radar image segmentation using fuzzy label field-based triplet Markov fields model’, IET Image Process., 2014, 8, (12), pp. 856869.
    15. 15)
      • 8. Shi, J.B., Malik, J.: ‘Normalized cuts and image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 888905.
    16. 16)
      • 3. Yang, D., Fei, R., Yao, J., et al: ‘Two-stage SAR image segmentation framework with an efficient union filter and multi-objective kernel clustering’, Appl. Soft Comput., 2016, 44, (11), pp. 3044.
    17. 17)
      • 20. Xiang, D.L., Tang, T., Hu, C.B., et al: ‘A kernel clustering algorithm with fuzzy factor: application to SAR image segmentation’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (7), pp. 12901294.
    18. 18)
      • 7. Ciecholewski, M.: ‘River channel segmentation in polarimetric SAR images: watershed transform combined with average contrast maximisation’, Expert Syst. Appl., 2017, 82, pp. 196215.
    19. 19)
      • 11. Zhang, P., Li, M., Wu, Y., et al: ‘Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model’, Pattern Recognit., 2012, 45, (11), pp. 40184033.
    20. 20)
      • 15. Do, M.N., Vetterli, M.: ‘The Contourlet transform: an efficient directional multiresolution image representation’, IEEE Trans. Image Process., 2005, 14, pp. 20912106.
    21. 21)
      • 21. Sun, R., Yan, X., Gao, J.: ‘Robust video fingerprinting scheme based on Contourlet hidden Markov tree model’, Optik Int. J. Light Electron Opt., 2017, 128, pp. 139147.
    22. 22)
      • 22. Li, J.C., Huang, B., Peng, Y.X.: ‘A modified method to configure the parameters of the bilateral filtering for synthetic aperture radar image speckle reduction’, Acta Phys. Sin., 2012, 61, (18), pp. 189501-1189501-8.
    23. 23)
      • 23. Weidner, U.: ‘Contribution to the assessment of segmentation quality for remote sensing applications’. Proc. 21st Congress for the Int. Society for Photogrammetry and Remote Sensing, 2008.
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