A Hierarchical Student’s t-Distributions Based Unsupervised SAR Image Segmentation Method
- Authors
- Zheng Y.[Zheng Y.]; Sun Y.[Sun Y.]; Sun L.[Sun L.]; Zhang H.[Zhang H.]; Jeon B.[Jeon B.]
- Issue Date
- 2019
- Publisher
- Springer
- Keywords
- Hierarchical student’s-t distributions; Nonlocally weighted mean template; SAR image segmentation; Structure tensor
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.11935 LNCS, pp.472 - 483
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 11935 LNCS
- Start Page
- 472
- End Page
- 483
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/11836
- DOI
- 10.1007/978-3-030-36189-1_39
- ISSN
- 0302-9743
- 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. © 2019, Springer Nature Switzerland AG.
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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