Segmentation of SAR image based on Kullback-Leibler distance and regular tessellation

被引：0
|
作者：
Zhao Q.-H. [1 ]
Gao J. [2 ]
Zhao X.-M. [1 ]
Li Y. [1 ]
机构：
[1] School of Geomatics, Liaoning Technical University, Fuxin
[2] China Sciences Group Remote Sensing Group Technology Co Ltd, Tianjin
来源：
Kongzhi yu Juece/Control and Decision | 2018年 / 33卷 / 10期
关键词：
KL distance; M-H algorithm; Regular tessellation; SAR image segmentation;
D O I：
10.13195/j.kzyjc.2017.0758
中图分类号：
学科分类号：
摘要：
In this paper, a segmentation method for synthetic aperture radar(SAR) images based on Kullback-Leibler(KL) distance and regular tessellation is proposed. Firstly, the image domain is divided into several sub-blocks by a regular tessellation, and the divided blocks are considered as basic processing units during segmentation. It is assumed that all pixels in a sub-block follow Gaussian distribution, while to modeling feature field of a given image. Then a general Potts model is utilized to model relationship between neighbor sub-blocks in label field. According to Bayes theorem, the posterior probability model is obtained by combining pixels' feature and sub-blocks' labels. Thereafter, the heterogeneity coefficient between classes is characterized with KL distance, and the corresponding probability distribution function is constructed by a non-constrained Gibbs distribution. Combining the posterior probability model and the non-constrained Gibbs distribution, the image segmentation model is established. In order to simulate the segmentation model, a Metropolis-Hastings(M-H) sampling method is designed, including the operations of changing label and splitting sub-blocks. By analyzing the segmentation results of the proposed algorithm and the comparing algorithms, the validity and superiority of the proposed algorithm are fully verified. © 2018, Editorial Office of Control and Decision. All right reserved.
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页码：1767 / 1774
页数：7
相关论文
共 28 条
  • [11] Yang D.D., Wang L., Hei X.H., Et al., An efficient automatic SAR image segmentation framework in AIS using kernel clustering index and histogram statistics, Applied Soft Computing, 16, 3, pp. 63-79, (2014)
  • [12] Liu J., Lei Y.J., Xing Y.Q., Et al., Fusion technique for SAR and gray visible image based on hidden Markov model in non-subsample shearlet transform domain, Control and Decision, 31, 3, pp. 453-457, (2016)
  • [13] Ni W.P., Yan W.D., Bian H., Et al., SARimage segmentation based on MRF model and morphlogical operation, Electronics Optics & Control, 18, 1, pp. 32-36, (2011)
  • [14] Tu S., Li Y., Su Y., Overiew of SAR image segmentation based on active contour model, Systems Engineering and Electronics, 37, 8, pp. 1754-1766, (2015)
  • [15] Xu H.X., Tian Z., Ding M.T., Multiscale segmentation for SAR image based on spectral clustering and mixture model, J of Image and Graphics, 15, 3, pp. 450-454, (2015)
  • [16] Hou Y.M., Guo L., A novel SAR image segmentation method based on Markov random field, J Electronics & Information Technology, 29, 5, pp. 1069-1072, (2007)
  • [17] Lin Y.C., Fu X.Y., Wang F., Et al., An oil spill segmentation algorithm for SAR imagery based on phase congruency, Science of Surveying and Mapping, 41, 3, pp. 91-95, (2016)
  • [18] Lin D., Xu X., Pan X.F., Et al., Segmentation of SAR image of MSTAR SAR chips based on attributed scattering center feather and Markov random filed, Geomatics and Information Science of Wuhan University, 39, 11, pp. 1314-1317, (2014)
  • [19] Hu Y.Y., Fan J.C., Wang J., SAR image unsupervised segmentation based on a modified fuzzy C-means slgorithm, 20166th Int Conf on Information Science and Technology, pp. 520-523, (2016)
  • [20] Wang Y., Li Y., Zhao Q.H., SAR image segmentation combined regular tessellation and M-H algorithm, Geomatics and Information Science ofWuhanUnivercity, 41, 11, pp. 1491-1497, (2016)
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