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信息与电子科学

一种新型的空域极化SAR数据相干斑滤波方法

  • 张静怡 ,
  • 雷斌 ,
  • 刘团结
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  • 1. 中国科学院空间信息处理与应用系统技术重点实验室, 北京 100190;
    2. 中国科学院电子学研究所, 北京 100190;
    3. 中国科学院研究生院, 北京 100049

收稿日期: 2011-12-29

  修回日期: 2012-03-13

  网络出版日期: 2013-01-15

基金资助

国家重大科技基础设施建设项目(CARSS/HK-C)资助

A new spatial speckle reduction method for polarimetric SAR data

  • ZHANG Jing-Yi ,
  • LEI Bin ,
  • LIU Tuan-Jie
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  • 1. Key Laboratory of Technology in Geo-spatial Information Procession and Application System, Chinese Academy of Sciences, Beijing 100190, China;
    2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
    3. Graduate University, Chinese Academy of Sciences, Beijing 100049, China

Received date: 2011-12-29

  Revised date: 2012-03-13

  Online published: 2013-01-15

摘要

提出一种新型的空域极化SAR数据相干斑滤波方法,改进了Sigma滤波并结合基于Freeman-Durden分解的Wishart非监督分类方法,在保留强点目标的同时,有效选择同质区域且具有相同散射类型的像素参与滤波.实验结果表明,在相干斑噪声抑制和边缘纹理细节信息保持方面,该方法均具有优越性,且能保持极化SAR数据的地物散射特性.

本文引用格式

张静怡 , 雷斌 , 刘团结 . 一种新型的空域极化SAR数据相干斑滤波方法[J]. 中国科学院大学学报, 2013 , 30(1) : 60 -67 . DOI: 10.7523/j.issn.1002-1175.2013.01.010

Abstract

In order to effectively reduce the speckle of PolSAR data,a new algorithm is proposed combing the improved sigma filter with unsupervised Wishart classification based on Freeman-Durden decomposition. Pixels within the sigma range and also in the same scattering category are selected and used to estimate the central pixel. The experiment results show that the proposed algorithm excels in both the suppression of the speckle and the preservation of the edges and texture details and that it can protect the scattering characteristics of targets.

参考文献

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[3] Goze S, Lopes A. A MMSE speckle filter for full resolution SAR polarimetric data[J].Journal of Electromagnetic Waves and Applications, 1993, 7(5): 717-737.

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[6] Lee J S. Improved sigma filter for speckle filtering of SAR imagery[J]. IEEE Transactions On Geoscience and Remote Sensing,2009,47(1): 202-213.

[7] Freeman A, Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.

[8] Lee J S, Grunes M R, Ainsworth T L, et al. Unsupervised classification using polarimetric decomposition and the complex wishart classifier[J].IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249-2258

[9] Vasile G, Trouve E, Lee J S, et al. Intensity-driven-adaptive-neighborhood technique for polarimetric and interferometric parameter estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4): 994-1003.

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