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A Hierarchical Student’s t-Distributions Based Unsupervised SAR Image Segmentation Method

  • Yuhui ZhengEmail author
  • Yahui Sun
  • Le Sun
  • Hui Zhang
  • Byeungwoo Jeon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)

Abstract

We introduce a finite mixture mode using hierarchical Student’s distributions, called hierarchical Student’s t-mixture model (HSMM), for SAR images segmentation. The main advantages of the proposed method are as follows: first, in HSMM, the clustering problem is reformulated as a set of sub-clustering problems each of which can be solved by the traditional SMM algorithm. Second, a novel image content-adaptive mean template is introduced into HSMM to increase its robustness. Third, an expectation maximization algorithm is utilized for HSMM parameters estimation. Finally, experiments show that the HSMM is effective and robust.

Keywords

SAR image segmentation Hierarchical student’s-t distributions Structure tensor Nonlocally weighted mean template 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuhui Zheng
    • 1
    Email author
  • Yahui Sun
    • 1
  • Le Sun
    • 1
  • Hui Zhang
    • 2
  • Byeungwoo Jeon
    • 3
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  2. 2.School of TechnologyNanjing Audit UniversityNanjingPeople’s Republic of China
  3. 3.College of Information and Communication EngineeringSungkyunkwan UniversitySuwonKorea

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