属性散射中心匹配及其在SAR目标识别中的应用

丁柏圆 文贡坚 余连生 马聪慧

丁柏圆, 文贡坚, 余连生, 马聪慧. 属性散射中心匹配及其在SAR目标识别中的应用[J]. 雷达学报, 2017, 6(2): 157-166. doi: 10.12000/JR16104
引用本文: 丁柏圆, 文贡坚, 余连生, 马聪慧. 属性散射中心匹配及其在SAR目标识别中的应用[J]. 雷达学报, 2017, 6(2): 157-166. doi: 10.12000/JR16104
Ding Baiyuan, Wen Gongjian, Yu Liansheng, Ma Conghui. Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition[J]. Journal of Radars, 2017, 6(2): 157-166. doi: 10.12000/JR16104
Citation: Ding Baiyuan, Wen Gongjian, Yu Liansheng, Ma Conghui. Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition[J]. Journal of Radars, 2017, 6(2): 157-166. doi: 10.12000/JR16104

属性散射中心匹配及其在SAR目标识别中的应用

doi: 10.12000/JR16104
基金项目: 

教育部新世纪优秀人才支持计划 NCET-11-0866

详细信息
    作者简介:

    丁柏圆 (1990–), 男, 博士研究生, 研究方向为SAR自动目标识别。E-mail:Dingbaiyuan_NUDT@163.com

    文贡坚 (1972–), 男, 教授, 博士生导师, 研究方向为遥感图像处理

    余连生 (1984–), 男, 助理工程师, 研究方向为摄影测量与遥感

    马聪慧 (1987–), 女, 1987年生, 博士研究生, 研究方向为SAR自动目标识别

    通讯作者:

    丁柏圆, E-mail:Dingbaiyuan_NUDT@163.com

  • 中图分类号: TN957

Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition

Funds: 

The Program for New Century Excellent Talents in University NCET-11-0866

  • 摘要: 属性散射中心是合成孔径雷达 (Synthetic Aperture Radar, SAR) 图像的一个重要特征。该文提出了一种属性散射中心匹配方法并将其运用于SAR目标识别中。该方法首先基于属性散射中心模型提取待识别SAR图像和模板SAR图像的属性散射中心,进而采用Hungarian算法实现散射中心的匹配。在建立的匹配关系的基础上,设计了一种稳健的散射中心匹配度度量方法计算待识别散射中心与各类模板散射中心的匹配度。该匹配度准则充分考虑了单个散射中心强弱、匹配对强弱以及漏警、虚警带来的影响,对于散射中心集的匹配度的评价更为全面。基于Moving and Stationary Target Acquisition and Recognition (MSTAR) 数据集的实验验证了方法的有效性。

     

  • 图  1  一幅BMP2 SAR图像的参数估计重构结果 (动态范围40 dB)

    Figure  1.  The reconstruction of a BMP2 SAR image (Dynamic range: 40 dB)

    图  2  Hungarian算法实现散射中心匹配

    Figure  2.  The matching of attributed scattering centers by Hungarian algorithm

    图  3  门限计算

    Figure  3.  The calculation of the threshold

    图  4  “强匹配对”的权值计算

    Figure  4.  The weight of the "strong assignments"

    图  5  漏警和虚警的权值计算

    Figure  5.  The weight of MAs and FAs

    图  6  本文方法的基本流程

    Figure  6.  The general procedure of the proposed method

    图  7  本文方法在不同模型阶数的识别性能

    Figure  7.  The recognition performance under different model orders

    图  8  不同分辨率下的MSTAR图像

    Figure  8.  The MSTAR images under different resolutions

    图  9  不同分辨率下的识别率

    Figure  9.  The recognition performance under different resolutions

    图  10  不同信噪比下的MSTAR图像

    Figure  10.  The reconstructed MSTAR images under different SNRs

    图  11  不同信噪比下的识别性能

    Figure  11.  The recognition performance under different SNRs

    表  1  分配代价矩阵

    Table  1.   The cost matrix for Hungarian matching

    Y1 Y2 $\begin{array}{*{20}{c}}\cdots \end{array}$ Yn MA
    X1 C11 C12 $\begin{array}{*{20}{c}}\cdots \end{array}$ C1n M1 $\infty $ $\begin{array}{*{20}{c}}\cdots \end{array}$ $\infty $
    X2 C21 C22 $\begin{array}{*{20}{c}}\cdots \end{array}$ C2n $\infty $ M2 $\begin{array}{*{20}{c}}\cdots \end{array}$ $\infty $
    $\vdots $ $\vdots $ $\vdots $ $ \ddots $ $\vdots $ $\vdots $ $\vdots $ $ \ddots $ $\vdots $
    Xm Cm1 Cm2 $\begin{array}{*{20}{c}}\cdots \end{array}$ Cmn $\infty $ $\infty $ $\begin{array}{*{20}{c}}\cdots \end{array}$ Mm
    FA F1 $\infty $ $\ldots $ $\infty $ $\infty $ $\infty $ $\ldots $ $\infty $
    $\infty $ F2 $\ldots $ $\infty $ $\infty $ $\infty $ $\ldots $ $\infty $
    $\vdots $ $\vdots $ $ \ddots $ $\vdots $ $\vdots $ $\vdots $ $ \ddots $ $\vdots $
    $\infty $ $\infty $ $\ldots$ Fn $\infty $ $\infty $ $\ldots$ $\infty $
    下载: 导出CSV

    表  2  属性的不确定性建模

    Table  2.   The modeling of attribute uncertainty

    属性类别 均值 方差
    方位向x x0 ${\sigma ^2} = {\rm{CR}}{{\rm{R}}^2}$
    距离向y y0 ${\sigma ^2} = {\rm{R}}{{\rm{R}}^2}$
    归一化幅度|A| |A0| ${\sigma ^2} = 0.1$
    下载: 导出CSV

    表  3  模板集和测试集

    Table  3.   The template set and testing set

    模板集 样本数量 测试集 样本数量
    BMP2(SN_C21) 233 BMP2(SN_C21) 195
    BMP2(SN_9566) 232 BMP2(SN_9566) 196
    BMP2(SN_9563) 233 BMP2(SN_9563) 196
    BTR70(SN_C71) 233 BTR70(SN_C71) 196
    T72(SN_132) 232 T72(SN_132) 196
    T72(SN_812) 231 T72(SN_812) 195
    T72(SN_S7) 228 T72(SN_S7) 191
    下载: 导出CSV

    表  4  本文方法的识别结果

    Table  4.   The recognition result of the proposed method

    目标类型 识别结果 Pc(%)
    BMP2 BTR70 T72
    BMP2(SN_9563) 191 2 2 97.95
    BMP2(SN_9566) 195 1 0 99.49
    BMP2(SN_C21) 192 2 2 97.96
    BTR70 5 188 3 95.92
    T72(SN_132) 8 1 187 96.91
    T72(SN_812) 1 0 194 99.49
    T72(SN_S7) 4 1 186 97.31
    Average (%) 97.88
    下载: 导出CSV

    表  5  3种方法的识别性能对比

    Table  5.   The comparison of the three methods

    方法 识别率 (%) 消耗时间 (ms)
    本文方法 97.88 10.2
    方法一 93.46 3.5
    方法二 96.92 20.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-09-15
  • 修回日期:  2016-11-25
  • 网络出版日期:  2017-04-28

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