基于黎曼流形的极化SAR图像分类

杨文 钟能 严天恒 杨祥立

杨文, 钟能, 严天恒, 杨祥立. 基于黎曼流形的极化SAR图像分类[J]. 雷达学报, 2017, 6(5): 433-441. doi: 10.12000/JR17031
引用本文: 杨文, 钟能, 严天恒, 杨祥立. 基于黎曼流形的极化SAR图像分类[J]. 雷达学报, 2017, 6(5): 433-441. doi: 10.12000/JR17031
Yang Wen, Zhong Neng, Yan Tianheng, Yang Xiangli. Classification of Polarimetric SAR Images Based on the Riemannian Manifold[J]. Journal of Radars, 2017, 6(5): 433-441. doi: 10.12000/JR17031
Citation: Yang Wen, Zhong Neng, Yan Tianheng, Yang Xiangli. Classification of Polarimetric SAR Images Based on the Riemannian Manifold[J]. Journal of Radars, 2017, 6(5): 433-441. doi: 10.12000/JR17031

基于黎曼流形的极化SAR图像分类

doi: 10.12000/JR17031
基金项目: 国家自然科学基金(61331016, 61271401)
详细信息
    作者简介:

    杨文:杨 文(1976–),男,教授,博士生导师,2004年获得武汉大学工学博士学位。研究方向为图像处理与计算机视觉。E-mail: yangwen@whu.edu.cn

    钟能:钟 能(1993–),男,2015年获得吉林大学工学学士学位,现于武汉大学电子信息学院攻读硕士学位。主要研究方向为极化合成孔径雷达图像处理。E-mail: zn_whu@whu.edu.cn

    严天恒(1995–),女,2016年获得武汉大学工学学士学位,现于武汉大学电子信息学院信号处理实验室攻读硕士学位。主要研究方向为极化合成孔径雷达图像解译。E-mail: yanth_eis@whu.edu.cn

    杨祥立(1991–),男,2016年获得武汉大学工学硕士学位,现于武汉大学电子信息学院攻读博士学位。主要研究方向为极化合成孔径雷达图像处理。E-mail: xiangliyang@whu.edu.cn

    通讯作者:

    杨文   yangwen@whu.edu.cn

  • 中图分类号: TN957

Classification of Polarimetric SAR Images Based on the Riemannian Manifold

Funds: The National Natural Science Foundation of China (61331016, 61271401)
  • 摘要: 分类是极化SAR图像解译的核心内容之一。一种新的思路是通过利用极化SAR协方差矩阵所形成的黎曼流形结构特性进行极化SAR图像分类。该文首先回顾了极化SAR图像分析中常用的黎曼流形测度,然后论述了如何对黎曼流形上的极化协方差矩阵进行稀疏编码。在监督分类方面,基于核空间黎曼流形稀疏编码提出了融合空间信息的极化SAR图像监督分类方法;在非监督分类方面,基于黎曼稀疏编码提出了利用黎曼稀疏诱导相似度的极化SAR图像非监督分类方法。在EMISAR和AIRSAR极化数据上的实验结果表明了该文所提方法的有效性。

     

  • 图  1  EMISAR数据的实验结果

    Figure  1.  The experiment results of EMISAR data

    图  2  AIRSAR数据的实验结果

    Figure  2.  The experiment results of AIRSAR data

    表  1  EMISAR数据监督分类结果

    Table  1.   The supervised classification results of EMISAR data

    方法 针叶林 小麦 油菜 燕麦 黑麦 OA Kappa
    Wishart方法 0.9370 0.9527 0.9466 0.9843 0.9904 0.9713 0.9623
    稀疏表达方法 0.9996 0.9860 0.9479 0.8946 0.9621 0.9472 0.9304
    KSC方法 1 1 0.9960 0.9982 0.9975 0.9981 0.9975
    下载: 导出CSV

    表  2  AIRSAR数据非监督分类结果

    Table  2.   The unsupervised classification results of AIRSAR data

    方法 OA F1-score Purity Entropy
    Wishart方法 0.6265 0.6084 0.7324 0.2909
    Bartlett方法 0.6538 0.6376 0.8015 0.2353
    RSC方法 0.8485 0.8633 0.9047 0.1344
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-03-24
  • 修回日期:  2017-06-08
  • 网络出版日期:  2017-07-12
  • 刊出日期:  2017-10-28

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