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基于字典学习和拓展联合动态稀疏表示的SAR目标识别

曹娜 王永利 孙建红 赵宁 宫小泽

曹娜, 王永利, 孙建红, 赵宁, 宫小泽.基于字典学习和拓展联合动态稀疏表示的SAR目标识别.自动化学报, 2020, 46(12): 2638-2646 doi: 10.16383/j.aas.c180228
引用本文: 曹娜, 王永利, 孙建红, 赵宁, 宫小泽.基于字典学习和拓展联合动态稀疏表示的SAR目标识别.自动化学报, 2020, 46(12): 2638-2646 doi: 10.16383/j.aas.c180228
Cao Na, Wang Yong-Li, Sun Jian-Hong, Zhao Ning, Gong Xiao-Ze. SAR target recognition based on dictionary learning and extended joint dynamic sparse representation. Acta Automatica Sinica, 2020, 46(12): 2638-2646 doi: 10.16383/j.aas.c180228
Citation: Cao Na, Wang Yong-Li, Sun Jian-Hong, Zhao Ning, Gong Xiao-Ze. SAR target recognition based on dictionary learning and extended joint dynamic sparse representation. Acta Automatica Sinica, 2020, 46(12): 2638-2646 doi: 10.16383/j.aas.c180228

基于字典学习和拓展联合动态稀疏表示的SAR目标识别

doi: 10.16383/j.aas.c180228
基金项目: 

国家自然科学基金 61170035

国家自然科学基金 61272420

国家自然科学基金 81674099

中央高校基本科研业务费专项资金项目 30916011328

中央高校基本科研业务费专项资金项目 30918015103

南京市科技计划项目 201805036

详细信息
    作者简介:

    曹娜 南京理工大学计算机科学与工程学院硕士研究生.主要研究方向为模式识别和稀疏表示. E-mail: 116106000721@njust.edu.cn

    孙建红 南京理工大学电子工程与光电技术学院副教授.主要研究方向为电路信号检测与处理, 电路故障研究, 目标识别. E-mail: sunjh1210@sina.com

    赵宁 南京理工大学计算机科学与工程学院硕士研究生.主要研究方向为网络异常检测. E-mail: johnny_mail@foxmail.com

    宫小泽 高级工程师.陆军安全委员会专家库成员.主要研究方向为数据治理, 弹药毁伤评估, 网络空间安全. E-mail: gongxiaoze2003@sina.com

    通讯作者:

    王永利 博士, 南京理工大学计算机科学与工程学院教授.主要研究方向为海量数据分析, 机器学习, 自然语言处理, 模式识别.本文通信作者. E-mail: yongliwang@njust.edu.cn

  • 本文责任编委  桑农

SAR Target Recognition Based on Dictionary Learning and Extended Joint Dynamic Sparse Representation

Funds: 

National Natural Science Foundation of China 61170035

National Natural Science Foundation of China 61272420

National Natural Science Foundation of China 81674099

Fundamental Research Fund for the Central Universities 30916011328

Fundamental Research Fund for the Central Universities 30918015103

Nanjing Science and Technology Development Plan Project 201805036

More Information
    Author Bio:

    CAO Na  Master student at the School of Computer Science and Engineering, Nanjing University of Science and Technology. Her research interest covers pattern recognition and sparse representation

    SUN Jian-Hong  Associate professor at the School of Electronic and Optical Engineering, Nanjing University of Science and Technology. Her research interest covers circuit signal detection and processing, circuit fault research, and target recognition

    ZHAO Ning  Master student at the School of Computer Science and Engineering, Nanjing University of Science and Technology. His main research interest is network anomaly detection

    GONG Xiao-Ze  Senior engineer, member of the Army Security Council Expert Pool. His research interest covers data management, ammunition damage assessment, and cyberspace security

    Corresponding author: WANG Yong-Li  Ph. D, professor at the School of Computer Science and Engineering. Nanjing University of Science and Technology. His research interest covers massive data analysis, machine learning, natural language processing, and pattern recognition. Corresponding author of this paper
  • Recommended by Associate Editor SANG Nong
  • 摘要: 提出了一种基于字典学习和拓展联合动态稀疏表示的合成孔径雷达(Synthetic aperture radar, SAR)图像的目标自动识别(Automatic target recognition, ATR)方法.首先, 在图像预处理时, 分割出目标区域和目标遮挡地面形成的阴影区域, 将这两个区域的信息结合起来能更好地表示图像.其次, 将字典学习方法LC-KSVD (Label consistent k-singular value decomposition)引入到训练阶段中, 分别学习目标区域和阴影区域的特征字典, 而不是直接将所有训练样本作为固定字典.最后, 在测试阶段提出了拓展联合动态稀疏表示算法, 使图像数据中的两个特征共享相似但不完全相同的稀疏模式, 还可处理图像噪声遮挡损坏问题.标准数据集上的实验结果表明, 该方法使不同类别更具区分性, 有效地提高了SAR图像的目标识别准确度.
    Recommended by Associate Editor SANG Nong
    1)  本文责任编委  桑农
  • 图  1  基于字典学习和拓展联合动态稀疏表示的SAR目标识别方法流程图

    Fig.  1  Flowchart of SAR target recognition based on dictionary learning and extended joint dynamic sparse representation

    图  2  SAR图像预处理流程

    Fig.  2  The preprocessing process of SAR images

    图  3  不同稀疏模型的系数矩阵

    Fig.  3  Illustration of coefficient matrix $\mathit{\boldsymbol{X}}$ of different sparse models

    图  4  SAR图像(以BMP2为例)预处理结果

    Fig.  4  Preprocessing result of SAR image (take BMP2 as an example)

    图  5  不同方法的识别正确率

    Fig.  5  Recognition accuracy of different methods

    图  6  不同稀疏度下识别正确率随字典尺寸变化的情况

    Fig.  6  The variety trend of recognition accuracy with the dictionary size under different sparsity

    表  1  样本的类别和数量

    Table  1  Categories and quantities of samples

    训练样本 样本数量 测试样本 样本数量
    BMP2-SN9563 233 BMP2-SN9563 195
    BMP2-SN9566 196
    BMP2-SNC21 196
    BTR70-SNC71 233 BTR70-SNC71 196
    T72-SN132 233 T72-SN132 196
    T72-SN812 195
    T72-SNS7 191
    总数 698 总数 1 365
    下载: 导出CSV

    表  2  DL + EJDSR方法的识别结果

    Table  2  The identification result of DL + EJDSR

    型号 BMP2 BTR70 T72 识别正确率(%)
    BMP2-SN9563 190 5 0 97.44
    BMP2-SN9566 181 6 9 92.35
    BMP2-SNC21 182 4 10 92.85
    BTR70-SNC71 0 196 196 100
    T72-SN132 0 0 196 100
    T72-SN812 9 12 174 89.23
    T72-SNS7 20 6 165 86.39
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-19
  • 录用日期:  2018-11-05
  • 刊出日期:  2020-12-29

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