J4 ›› 2015, Vol. 42 ›› Issue (3): 15-21+89.doi: 10.3969/j.issn.1001-2400.2015.03.003

• 研究论文 • 上一篇    下一篇

一种SAR目标属性特征提取算法

李飞;纠博;王英华;罗涛;刘宏伟   

  1. (西安电子科技大学 雷达信号处理国家重点实验室,陕西 西安  710071)
  • 收稿日期:2014-01-02 出版日期:2015-06-20 发布日期:2015-07-27
  • 通讯作者: 李飞
  • 作者简介:李飞(1984-),男,西安电子科技大学博士研究生,E-mail:xdlifei@gmail.com.
  • 基金资助:

    国家自然科学基金资助项目(61271024,61201292, 61201283);新世纪优秀人才支持计划资助项目(NCET-09-0630);全国优秀博士学位论文作者专项资金资助项目(FANEDD-201156);中央高校基本科研业务费专项资金联合资助项目

Novel method for attributed feature extraction from SAR imagery

LI Fei;JIU Bo;WANG Yinghua;LUO Tao;LIU Hongwei   

  1. (National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an  710071, China)
  • Received:2014-01-02 Online:2015-06-20 Published:2015-07-27
  • Contact: LI Fei

摘要:

针对散射中心重叠的情况,利用散射中心空域及其散射机理的稀疏特性,提出一种基于全极化属性散射中心模型的合成孔径雷达目标属性特征提取算法.根据散射中心空域与散射机理的稀疏特性,对目标的极化分解系数矩阵分别施加行稀疏约束与矩阵稀疏约束.由于极化散射机理字典包含未知参数,在此采用坐标轮回下降法分别估计极化分解系数矩阵与极化散射机理字典,同时提取属性散射中心及其极化特征等属性特征.基于电磁计算数据的实验结果,验证了该算法能够利用极化信息提取散射中心的属性特征.

关键词: 合成孔径雷达自动目标识别, 极化, 特征提取, 稀疏

Abstract:

Aiming to extract attributed features of overlapped attributed scattering centers, a new method for attributed feature extraction of the SAR target is proposed based on the fully polarimetric attributed scattering center model by considering the sparsity of the scattering center in the space domain and scattering mechanism domain. According to this sparsity, the row sparse constraint and matrix sparse constraint are imposed on the polarimetric decomposition coefficient matrix of the target, respectively. Since the polarimetric scattering mechanism dictionary contains an unknown parameter, the coordinate decent technique is employed to optimize the polarimetric decomposition coefficient matrix and polarimetric scattering mechanism dictionary for attributed feature extraction (attributed scattering center and polarimetric signature). Numerical results on electromagnetic computation data verify the validity of the proposed algorithm.

Key words: synthetic aperture radar automatic target recognition, polarimetry, feature extraction, sparse

中图分类号: 

  • TN957
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