A Nonlocal TV-Based Variational Method for PolSAR Data Speckle Reduction
Nie, Xiangli1; Qiao, Hong1,2; Zhang, Bo3; Huang, Xiayuan3; Bo Zhang
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2016-06-01
Volume25Issue:6Pages:2620-2634
SubtypeArticle
AbstractIn this paper, we propose a nonlocal total variation (NLTV)-based variational model for polarimetric synthetic aperture radar (PolSAR) data speckle reduction. This model, named WisNLTV, is obtained based on the Wishart fidelity term and the NLTV regularization defined for the complex-valued fourth-order tensor data. Since the proposed model is non-convex, an equivalent bi-convex model is obtained using the property of conjugate functions. Then, an efficient iteration algorithm is developed to solve the equivalent bi-convex model, based on the alternating minimization and the forward-backward operator splitting technique. The proposed iteration algorithm is proved to be convergent under certain conditions theoretically and numerically. Experimental results on both synthetic and real PolSAR data demonstrate that the proposed method can effectively reduce speckle noise and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.
KeywordPolarimetric Synthetic Aperture Radar (Polsar) Speckle Reduction Nonlocal Total Variation (Nltv) Complex Wishart Distribution Conjugate Function Variational Model
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2016.2552402
WOS KeywordPOLARIMETRIC SAR DATA ; LOJASIEWICZ INEQUALITY ; MULTIPLICATIVE NOISE ; WISHART DISTRIBUTION ; MODEL ; ALGORITHMS ; CLASSIFICATION ; DECOMPOSITION ; FRAMEWORK ; IMAGERY
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61379093 ; Strategic Priority Research Program, Chinese Academy of Sciences(XDB02080003) ; BMST(D16110400140000 ; Early Career Development Award of SKLMCCS ; 11131006) ; D161100001416001)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000375121600001
Citation statistics
Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12207
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorBo Zhang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
3.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Nie, Xiangli,Qiao, Hong,Zhang, Bo,et al. A Nonlocal TV-Based Variational Method for PolSAR Data Speckle Reduction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(6):2620-2634.
APA Nie, Xiangli,Qiao, Hong,Zhang, Bo,Huang, Xiayuan,&Bo Zhang.(2016).A Nonlocal TV-Based Variational Method for PolSAR Data Speckle Reduction.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(6),2620-2634.
MLA Nie, Xiangli,et al."A Nonlocal TV-Based Variational Method for PolSAR Data Speckle Reduction".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.6(2016):2620-2634.
Files in This Item: Download All
File Name/Size DocType Version Access License
TIP2016.pdf(5355KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
[Zhang, Bo]'s Articles
Baidu academic
Similar articles in Baidu academic
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
[Zhang, Bo]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
[Zhang, Bo]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: TIP2016.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.