SAR ATR中标签噪声不确定性建模与纠正

于跃 王琛 师君 陶重犇 李良 唐欣欣 周黎明 韦顺军 张晓玲

于跃, 王琛, 师君, 等. SAR ATR中标签噪声不确定性建模与纠正[J]. 雷达学报(中英文), 2024, 13(5): 974–984. doi: 10.12000/JR24130
引用本文: 于跃, 王琛, 师君, 等. SAR ATR中标签噪声不确定性建模与纠正[J]. 雷达学报(中英文), 2024, 13(5): 974–984. doi: 10.12000/JR24130
YU Yue, WANG Chen, SHI Jun, et al. Modeling and correction of label noise uncertainty for SAR ATR[J]. Journal of Radars, 2024, 13(5): 974–984. doi: 10.12000/JR24130
Citation: YU Yue, WANG Chen, SHI Jun, et al. Modeling and correction of label noise uncertainty for SAR ATR[J]. Journal of Radars, 2024, 13(5): 974–984. doi: 10.12000/JR24130

SAR ATR中标签噪声不确定性建模与纠正

DOI: 10.12000/JR24130 CSTR: 32380.14.JR24130
基金项目: 国家自然科学基金(62201375),江苏省自然科学基金(BK20220635),重庆市自然科学基金面上项目 (CSTB2024NSCQ-MSX1762),重庆市教育委员会科学技术研究项目(KJQN202300756)
详细信息
    作者简介:

    于 跃,硕士生,主要研究方向为噪声标签学习、半监督学习

    王 琛,博士,讲师,主要研究方向为雷达目标检测与识别、噪声标签学习、半监督学习

    师 君,博士,副教授,主要研究方向为雷达信号处理、雷达系统、雷达图像处理、深度学习

    陶重犇,博士,副教授,主要研究方向为深度学习、三维目标检测

    李 良,博士,工程师,主要研究方向为合成孔径雷达成像、雷达图像处理、合成孔径雷达干扰对抗

    唐欣欣,博士,讲师,主要研究方向为雷达信号处理、雷达成像

    周黎明,博士,工程师,主要研究方向为三维SAR成像、多通道高分宽幅SAR成像、新体制雷达信号处理等

    韦顺军,博士,教授,主要研究方向为雷达信号处理、雷达系统

    张晓玲,博士,教授,主要研究方向为雷达信号处理、雷达目标分类识别

    通讯作者:

    王琛 chenwang@usts.edu.cn

    师君 shijun@uestc.edu.cn

  • 责任主编:张帆 Corresponding Editor: ZHANG Fan
  • 中图分类号: TN957.52

Modeling and Correction of Label Noise Uncertainty for SAR ATR

Funds: The National Natural Science Foundation of China (62201375), Natural Science Foundation of Jiangsu Province (BK20220635), Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1762), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300756)
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  • 摘要: 深度监督学习在合成孔径雷达自动目标识别任务中的成功依赖于大量标签样本。但是,在大规模数据集中经常存在错误(噪声)标签,很大程度降低网络训练效果。该文提出一种基于损失曲线拟合的标签噪声不确定性建模和基于噪声不确定度的纠正方法:以损失曲线作为判别特征,应用无监督模糊聚类算法获得聚类中心和类别隶属度以建模各样本标签噪声不确定度;根据样本标签噪声不确定度将样本集划分为噪声标签样本集、正确标签样本集和模糊标签样本集,以加权训练损失方法分组处理训练集,指导分类网络训练实现纠正噪声标签。在MSTAR数据集上的实验证明,该文所提方法可处理数据集中混有不同比例标签噪声情况下的网络训练问题,有效纠正标签噪声。当训练数据集中标签噪声比例较小(40%)时,该文所提方法可纠正98.6%的标签噪声,并训练网络达到98.7%的分类精度。即使标签噪声比例很大(80%)时,该文方法仍可纠正87.8%的标签噪声,并训练网络达到82.3%的分类精度。

     

  • 图  1  混合正确和噪声标签样本训练分类网络的损失曲线(以含80%标签噪声数据集训练ResNet18网络300次获得)

    Figure  1.  Loss curves of the classification network trained by mixed-noisy-and-clean labels (obtained by training ResNet18 with 80% noisy labels after 300 epochs)

    图  2  混合正确和噪声标签样本训练分类网络的损失曲线(以含80%标签噪声数据集训练ResNet18网络300次获得)和聚类中心

    Figure  2.  Loss curves of the classification network trained by mixed-noisy-and-clean labels (obtained by training ResNet18 with 80% noisy labels after 300 epochs) and clustering centers for clean and noisy labels

    图  3  标签纠正示例

    Figure  3.  Demo of label correction

    图  4  本文方法训练分类网络的损失曲线

    Figure  4.  Loss curves of clean and noisy samples for our proposed method

    图  5  分类精度曲线

    Figure  5.  Classification accuracy curves

    表  1  标签噪声数据集训练分类网络模型精度(%)

    Table  1.   Classification accuracy of the models trained with noisy labels and clean labels (%)

    训练网络模型 分类精度
    20%的正确标签 92.13
    混合20%的正确标签 & 80%的噪声标签 27.46
    100%的正确标签 98.30
    下载: 导出CSV

    1  含噪标签数据集训练分类网络

    1.   Train a classification network with noisy labels

     输入:X:训练集中的图像样本。
        ${Y_{\mathrm{n}}}$:训练集中的样本标签(包括噪声标签)。
        $ f\left( { \cdot ;\theta } \right) $:一个分类网络(本文中为CNN)。
     输出:CNN训练权重$ {\theta ^{{T_2}}} $
     步骤1:混有噪声和正确标签数据集$(X,{Y_{\mathrm{n}}})$训练网络${T_1}$次
     获得$ f\left( { \cdot ;{\theta ^{{T_1}}}} \right) $和$\left\{ {{{\boldsymbol{l}}}_i^{{T_1}}} \right\}_{i = 1}^N$。
     步骤2:标签噪声不确定性建模
     初始化c
     for i in $ \left[1,T_c\right] $ iterations: do
      根据式(2)计算${\boldsymbol{c}}(k)$
      根据式(3)计算${\mu _i}(k)$
     end for
     获得${{\boldsymbol{c}}^ * }(k)$和$ \mu _i^ * (k) $
     步骤3:基于噪声不确定度的数据划分
     获得噪声样本集$ {D_{\mathrm{n}}} $、正确样本集$ {D_{\mathrm{c}}} $、模糊样本集$ {D_{\mathrm{f}}} $
     $ {D_{\text{n}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|\mu _i^ * (2) > {\tau _1}} \right\}_{i = 1}^N $
     $ {D_{\mathrm{c}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|\mu _i^ * (2) < {\tau _2}} \right\}_{i = 1}^N $
     $ {D_{\mathrm{f}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|{\tau _2} \le \mu _i^ * (2) \le {\tau _1}} \right\}_{i = 1}^N $
     通过mixup数据增强获得$ {D_{{\text{aug}}}} $
     根据式(5)计算${{\boldsymbol{x}}_{{\mathrm{mix}}}}$
     根据式(6)计算${{\boldsymbol{y}}_{{\mathrm{mix}}}}$
     $ {D_{{\text{aug}}}} = \left\{ {\left( {{{\boldsymbol{x}}_{{\text{mix}}}},{{\boldsymbol{y}}_{{\text{mix}}}}} \right)} \right\} $
     $ {D_{\mathrm{m}}} = {D_{\text{c}}} \cup {D_{\mathrm{f}}} \cup {D_{{\mathrm{aug}}}} $
     步骤4:使用${D_{\mathrm{m}}}$分组加权纠正训练网络${T_2}$次
    下载: 导出CSV

    表  2  MSTAR数据集中训练和测试数据集中的目标数量

    Table  2.   Number of targets in the training and testing datasets of the MSTAR dataset

    地面目标图像训练数据集测试数据集总数据集
    2S1299274573
    BMP2233195428
    BRDM2298274572
    BTR60256195451
    BTR70233196429
    D7299274573
    T62299273572
    T72232196428
    ZIL131299274573
    ZSU234299274573
    下载: 导出CSV

    表  3  不同比例标签噪声下的纠正精度(%)

    Table  3.   The correction accuracy with different noise ratio (%)

    算法 40 (36.4) 60 (54.0) 80 (72.5)
    BMM-based 98.7 84.8 61.1
    LNMC 97.9 92.2 78.1
    本文方法 98.6 97.2 87.8
    注:由于制作标签噪声时一些样本标签可能被随机重新标注为正确标签,实际噪声比例略低于设置比例,括号内为实验中真实标签噪声比例。加粗项表示最优结果。
    下载: 导出CSV

    表  4  不同比例标签噪声下的网络分类精度(%)

    Table  4.   The classification accuracy with different noise ratio (%)

    算法 40 (36.4) 60 (54.0) 80 (72.5)
    mixup[20] 89.9 65.6 56.9
    BMM-based[8] 97.3 80.8 64.5
    RNSL[17] 46.1 45.9 41.2
    LNMC[36] 96.8 87.6 79.6
    Prune4ReL[30] 88.4 84.0 72.7
    本文方法 98.7 94.9 82.3
    注:由于制作标签噪声时一些样本标签可能被随机重新标注为正确标签,实际噪声比例略低于设置比例,括号内为实验中真实标签噪声比例。加粗项表示最优结果。
    下载: 导出CSV

    表  5  噪声比80%下$ {{\boldsymbol{\tau}} _1},{\boldsymbol{{\tau}} _2} $不同取值时的网络分类精度

    Table  5.   The classification accuracy with different $ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $ values under 80% noise ratio

    $ {\tau _1} $$ {\tau _2} $分类精度(%)
    0.70.180.8
    0.80.180.9
    0.90.180.9
    0.70.282.7
    0.80.282.3
    0.90.282.5
    0.70.382.2
    0.80.382.3
    0.90.382.3
    下载: 导出CSV

    表  6  噪声比40%下$ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $不同取值时的网络分类精度

    Table  6.   The classification accuracy with different $ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $ values under 40% noise ratio

    $ {\tau _1} $$ {\tau _2} $分类精度(%)
    0.70.196.4
    0.80.196.8
    0.90.196.6
    0.70.297.6
    0.80.298.7
    0.90.297.5
    0.70.397.6
    0.80.397.6
    0.90.397.4
    下载: 导出CSV

    表  7  Mixup方法消融实验(%)

    Table  7.   Ablation study on mixup method (%)

    是否使用mixup 40 (36.4) 60 (54.0) 80 (72.5)
    94.7 90.3 73.2
    98.7 94.9 82.3
    注:由于制作标签噪声时一些样本标签可能被随机重新标注为正确标签,实际噪声比例略低于设置比例,括号内为实验中真实标签噪声比例。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-06-27
  • 修回日期:  2024-08-11
  • 网络出版日期:  2024-09-05
  • 刊出日期:  2024-09-28

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