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Journal of Applied Remote Sensing

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field
Author(s): Junfei Shi; Lingling Li; Fang Liu; Licheng Jiao; Hongying Liu; Shuyuan Yang; Lu Liu; Hong-Xia Hao
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Paper 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.

Paper Details

Date Published: 2 May 2016
PDF: 20 pages
J. Appl. Rem. Sens. 10(2) 025008 doi: 10.1117/1.JRS.10.025008
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Junfei Shi, Xidian Univ. (China)
Lingling Li, Xidian Univ. (China)
Fang Liu, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)
Hongying Liu, Xidian Univ. (China)
Shuyuan Yang, Xidian Univ. (China)
Lu Liu, Xidian Univ. (China)
Hong-Xia Hao, Xidian Univ. (China)

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