基于特征分解卷积神经网络的SAR图像目标检测方法

李毅 杜兰 杜宇昂

李毅, 杜兰, 杜宇昂. 基于特征分解卷积神经网络的SAR图像目标检测方法[J]. 雷达学报, 2023, 12(5): 1069–1080. doi: 10.12000/JR23004
引用本文: 李毅, 杜兰, 杜宇昂. 基于特征分解卷积神经网络的SAR图像目标检测方法[J]. 雷达学报, 2023, 12(5): 1069–1080. doi: 10.12000/JR23004
LI Yi, DU Lan, and DU Yuang. Convolutional neural network based on feature decomposition for target detection in SAR images[J]. Journal of Radars, 2023, 12(5): 1069–1080. doi: 10.12000/JR23004
Citation: LI Yi, DU Lan, and DU Yuang. Convolutional neural network based on feature decomposition for target detection in SAR images[J]. Journal of Radars, 2023, 12(5): 1069–1080. doi: 10.12000/JR23004

基于特征分解卷积神经网络的SAR图像目标检测方法

doi: 10.12000/JR23004
基金项目: 国家自然科学基金(U21B2039)
详细信息
    作者简介:

    李 毅,博士生,主要研究方向为SAR图像解译、机器学习与人工智能等

    杜 兰,博士,教授,主要研究方向为雷达目标识别、雷达信号处理、机器学习等

    杜宇昂,博士生,主要研究方向为SAR图像解译、机器学习与人工智能等

    通讯作者:

    杜兰 dulan@mail.xidian.edu.cn

  • 责任主编:朱卫纲 Corresponding Editor: ZHU Weigang
  • 中图分类号: TN957.51

Convolutional Neural Network Based on Feature Decomposition for Target Detection in SAR Images

Funds: The National Natural Science Foundation of China (U21B2039)
More Information
  • 摘要: 真实场景的高分辨率合成孔径雷达(SAR)图像大多是复杂的,对于地物场景来说,其背景中存在草地、树木、道路和建筑物等杂波,这些复杂背景杂波使得传统SAR图像目标检测算法的结果包含大量虚警和漏警,严重影响了SAR目标检测性能。该文提出一种基于特征分解卷积神经网络(CNN)的SAR图像目标检测方法,该方法在特征提取模块对输入图像提取特征后,通过特征分解模块分解出鉴别特征和干扰特征,最后将鉴别特征输入到多尺度检测模块进行目标检测。特征分解后去除的干扰特征是对目标检测不利的部分,其中包括复杂背景杂波,而保留的鉴别特征是对目标检测有利的部分,其中包括感兴趣目标,从而有效降低虚警和漏警,提高SAR目标检测性能。该文所提方法在MiniSAR实测数据集和SAR飞机检测实测数据集(SADD)上的F1-score值分别为0.9357和0.9211,与不加特征分解模块的单步多框检测器相比,所提方法的F1-score值分别提升了0.0613和0.0639。基于实测数据集的实验结果证明了所提方法对复杂场景SAR图像进行目标检测的有效性。

     

  • 图  1  基于特征分解CNN的SAR图像目标检测流程图

    Figure  1.  Flow chart of SAR target detection method based on feature decomposition CNN

    图  2  特征分解模块结构图

    Figure  2.  Structure of feature decomposition module

    图  3  MiniSAR数据集中的两幅SAR图像

    Figure  3.  Two SAR images in the MiniSAR dataset

    图  4  SADD数据集中的样本图像[23]

    Figure  4.  Examples in the SADD[23]

    图  5  不同方法对MiniSAR数据集中第1张测试图片的检测结果

    Figure  5.  Detection results of the first test image in the MiniSAR dataset by different methods

    图  6  不同方法对MiniSAR数据集中第2张测试图片的检测结果

    Figure  6.  Detection results of the second test image in the MiniSAR dataset by different methods

    图  7  不同方法对SADD数据集中部分测试切片的检测结果

    Figure  7.  Detection results of some test chips in the SADD by different methods

    图  8  部分SAR子图像经过特征分解模块得到的鉴别特征图、干扰特征图和重构图

    Figure  8.  The discriminative feature maps and interfering feature maps and reconstruction maps obtained by the feature decomposition module for some SAR sub-images

    表  1  特征提取模块的详细结构

    Table  1.   Detailed structure of feature extraction module

    层数操作超参数
    1卷积,ReLUk = (3,3), s = 1, p = 1, n = 64
    2卷积,ReLUk = (3,3), s = 1, p = 1, n = 64
    3最大池化k = (2,2), s = 2, p = 0
    4卷积,ReLUk = (3,3), s = 1, p = 1, n = 128
    5卷积,ReLUk = (3,3), s = 1, p = 1, n = 128
    6最大池化k = (2,2), s = 2, p = 0
    7卷积,ReLUk = (3,3), s = 1, p = 1, n = 256
    8卷积,ReLUk = (3,3), s = 1, p = 1, n = 256
    9卷积,ReLUk = (3,3), s = 1, p = 1, n = 256
    10最大池化k = (2,2), s = 2, p = 0
    注:k, s, p, n分别为核尺寸、步长、填充数、核个数。
    下载: 导出CSV

    表  2  不同目标检测方法在MiniSAR数据集上的实验结果

    Table  2.   Experimental results of different target detection methods based on the MiniSAR dataset

    方法PrecisionRecallF1-score
    Gaussian-CFAR0.37890.79660.5135
    Faster R-CNN0.83700.91060.8723
    FPN0.86510.88620.8755
    SSD0.86290.88620.8744
    文献[25]的方法0.91340.94310.9280
    文献[26]的方法0.86860.93500.9006
    本文方法0.92060.95120.9357
    下载: 导出CSV

    表  3  不同目标检测方法在SADD数据集上的实验结果

    Table  3.   Experimental results of different target detection methods based on the SADD

    方法PrecisionRecallF1-score
    Gaussian-CFAR0.46100.52720.4919
    Faster R-CNN0.90360.80860.8535
    FPN0.85500.87030.8626
    SSD0.84640.86820.8572
    文献[26]的方法0.90140.87970.8904
    本文方法0.87880.96760.9211
    下载: 导出CSV

    表  4  消融实验结果

    Table  4.   Experimental results of ablation experiments

    实验编号损失定量评价
    正交损失重构损失PrecisionRecallF1-score
    1××0.86290.88620.8744
    2×0.90910.92680.9179
    3×0.89260.90240.8975
    40.92060.95120.9357
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-06
  • 修回日期:  2023-03-25
  • 网络出版日期:  2023-04-18
  • 刊出日期:  2023-10-28

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