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Review Article

WMFLICM: A Robust Algorithm for SAR Image Segmentation Using Hybrid Spatial Information

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References

  • K. Tomiyasu, “Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface,” Proc. IEEE, Vol. 66, no. 5, pp. 563–83, 1978. DOI: 10.1109/PROC.1978.10961.
  • S. Uhlmann, and S. Kiranyaz, “Classification of dual- and single polarized SAR images by incorporating visual features,” ISPRS. J. Photogramm. Remote. Sens., Vol. 90, pp. 10–22, Apr. 2014. DOI: 10.1016/j.isprsjprs.2014.01.005.
  • J.-S. Lee, “Speckle suppression and analysis for synthetic aperture radar images,” Opt. Eng., Vol. 25, no. 5, pp. 636–43, 1986. DOI: 10.1117/12.7973877.
  • J.-S. Lee, “Speckle analysis and smoothing of synthetic aperture radar images,” Comput. Graph. Image Process., Vol. 17, no. 1, pp. 24–32, 1981. DOI: 10.1016/S0146-664X(81)80005-6.
  • P. Wang, H. Zhang, and V. M. Patel, “SAR image despeckling using a convolutional neural network,” IEEE Signal Process Lett., Vol. 24, no. 12, pp. 1763–7, 2017. DOI: 10.1109/LSP.2017.2758203.
  • F. Mohammadimanesh, B. Salehi, M. Mahdianpari, E. Gill, and M. Molinier, “A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem,” ISPRS. J. Photogramm. Remote. Sens., Vol. 151, pp. 223–36, May 2019. DOI: 10.1016/j.isprsjprs.2019.03.015.
  • J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms. 1st ed. Boston, MA: Springer US, 1981. pp. 43–92.
  • M. S. Choudhry, and R. Kapoor, “Performance analysis of fuzzy C-means clustering methods for MRI image segmentation,” Procedia. Comput. Sci., Vol. 89, pp. 749–58, 2016. DOI: 10.1016/j.procs.2016.06.052.
  • M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Trans. Med. Imaging, Vol. 21, no. 3, pp. 193–99, 2002. DOI: 10.1109/42.996338.
  • S. Chen, and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Systems, Man, Cybernetics, Part B (Cybernetics), Vol. 34, no. 4, pp. 1907–16, 2004. DOI: 10.1109/TSMCB.2004.831165.
  • L. Szilagyi, Z. Benyo, S. M. Szilagyi, and H. S. Adam, “MR brain image segmentation using an enhanced fuzzy C-means algorithm,” Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1, pp. 724–26, 2003. Vol.1, DOI: 10.1109/IEMBS.2003.1279866.
  • W. Cai, S. Chen, and D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognit., Vol. 40, no. 3, pp. 825–38, 2007. DOI: 10.1016/j.patcog.2006.07.011.
  • S. Krinidis, and V. Chatzis, “A robust fuzzy local information C-means clustering algorithm,” IEEE Trans. Image Process., Vol. 19, no. 5, pp. 1328–37, May 2010. DOI: 10.1109/TIP.2010.2040763.
  • M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process., Vol. 22, no. 2, pp. 573–84, 2013. DOI: 10.1109/TIP.2012.2219547.
  • D. Xiang, T. Tang, C. Hu, Y. Li, and Y. Su, “A kernel clustering algorithm with fuzzy factor: application to SAR image segmentation,” IEEE Geosci. Remote Sens. Lett., Vol. 11, no. 7, pp. 1290–4, 2014. DOI: 10.1109/LGRS.2013.2292820.
  • S. Huang, W. Huang, and T. Zhang, “A new SAR image segmentation algorithm for the detection of target and shadow regions,” Sci. Rep., Vol. 6, no. 1, pp. 38596, 2016. DOI: 10.1038/srep38596.
  • R. C. P. Marques, F. N. Medeiros, and J. S. Nobre, “SAR image Segmentation based on level set approach and {G}A0 model,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 34, no. 10, pp. 2046–2057, 2012. DOI: 10.1109/TPAMI.2011.274.
  • MSTAR Dataset. Available: https://www.sdms.afrl.af.mil/index.php?collection=mstar (accessed Nov. 02, 2019).
  • A. C. Frery, A. D. C. Nascimento, and R. J. Cintra, “Contrast in speckled imagery with stochastic distances,” in 2010 IEEE International Conference on Image Processing, Hong Kong, China,  2010, pp. 69–72. DOI: 10.1109/ICIP.2010.5651315.
  • A. D. C. Nascimento, R. J. Cintra, and A. C. Frery, “Hypothesis testing in speckled data with stochastic distances,” IEEE Trans. Geosci. Remote Sens., Vol. 48, no. 1, pp. 373–85, 2010. DOI: 10.1109/TGRS.2009.2025498.

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