Image and Signal Processing Methods

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

[+] Author Affiliations
Junfei Shi, Fang Liu, Hongxia Hao

Xidian University, School of Computer Science and Technology, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, China

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, No. 2 South Taibai Road, Xian, Shaanxi Province 710071, China

Lingling Li, Licheng Jiao, Hongying Liu, Shuyuan Yang, Lu Liu

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, No. 2 South Taibai Road, Xian, Shaanxi Province 710071, China

J. Appl. Remote Sens. 10(2), 025008 (May 02, 2016). doi:10.1117/1.JRS.10.025008
History: Received October 29, 2015; Accepted March 30, 2016
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Abstract.  Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Junfei Shi ; Lingling Li ; Fang Liu ; Licheng Jiao ; Hongying Liu, et al.
"Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field", J. Appl. Remote Sens. 10(2), 025008 (May 02, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.025008


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