基于半监督空间-通道选择性卷积核网络的极化SAR图像地物分类

王睿川 王岩飞

王睿川, 王岩飞. 基于半监督空间-通道选择性卷积核网络的极化SAR图像地物分类[J]. 雷达学报, 2021, 10(4): 516–530. doi: 10.12000/JR21080
引用本文: 王睿川, 王岩飞. 基于半监督空间-通道选择性卷积核网络的极化SAR图像地物分类[J]. 雷达学报, 2021, 10(4): 516–530. doi: 10.12000/JR21080
WANG Ruichuan and WANG Yanfei. Terrain classification of polarimetric SAR images using semi-supervised spatial-channel selective kernel network[J]. Journal of Radars, 2021, 10(4): 516–530. doi: 10.12000/JR21080
Citation: WANG Ruichuan and WANG Yanfei. Terrain classification of polarimetric SAR images using semi-supervised spatial-channel selective kernel network[J]. Journal of Radars, 2021, 10(4): 516–530. doi: 10.12000/JR21080

基于半监督空间-通道选择性卷积核网络的极化SAR图像地物分类

doi: 10.12000/JR21080
基金项目: 国家重点研发计划(2017YFB0503001)
详细信息
    作者简介:

    王睿川(1994–),男,四川绵阳人,中国科学院空天信息创新研究院博士研究生,研究方向为SAR和极化SAR图像分割和解译、目标检测和识别等

    王岩飞(1963–),男,辽宁沈阳人,中国科学院空天信息创新研究院研究员,博士生导师,主要研究方向为微波成像雷达理论方法及应用、数字信号处理等

    通讯作者:

    王岩飞 yfwang@mail.ie.ac.cn

  • 责任主编:邹焕新 Corresponding Editor: ZOU Huanxin
  • 中图分类号: TN958

Terrain Classification of Polarimetric SAR Images Using Semi-supervised Spatial-channel Selective Kernel Network

Funds: The National Key Research and Development Program (2017YFB0503001)
More Information
  • 摘要: 针对极化合成孔径雷达(极化SAR)图像地物分类中标注样本数量少的问题,该文提出一种基于空间-通道选择性卷积核全卷积网络(SCSKFCN)和预选-联合优化半监督学习(SPUO)的极化SAR图像地物分类方法。SCSKFCN通过使用空间和通道注意力机制,对不同感受野的特征进行自适应加权融合,有效提升了模型的分类性能。SPUO能够高效地利用标注样本,挖掘无标注样本中蕴含的信息。它采用K-Wishart距离进行样本预选并生成伪标签,然后在联合优化阶段使用真实标注样本和伪标注样本同时对模型进行优化。在模型优化过程中,SPUO对伪标注样本进行两步验证并筛选可靠的伪标注样本参与优化。实验结果表明,该方法能够在只使用少量标注样本的条件下实现高精度、高效率的极化SAR图像地物分类。

     

  • 图  1  SKNet模块与空间注意力模块

    Figure  1.  SKNet module and spatial attention module

    图  2  空间-通道选择性卷积核(SCSK)单元

    Figure  2.  Spatial-Channel Selective Kernel (SCSK) unit

    图  3  空间-通道选择性卷积核全卷积网络(SCSKFCN)的架构

    Figure  3.  Architecture of Spatial-Channel Selective Kernel Fully Convolutional Network (SCSKFCN)

    图  4  联合优化运行流程图

    Figure  4.  Flowchart of united optimization

    图  5  预选-联合优化半监督学习方法流程图

    Figure  5.  The framework of semi-supervised preselection and united optimization method

    图  6  Flevoland图像分类结果图

    Figure  6.  Classification results of Flevoland image with different methods

    图  7  Oberpfaffenhofen图像分类结果图

    Figure  7.  Classification results of Oberpfaffenhofen image with different methods

    图  8  预测概率验证阈值$\delta $对性能的影响

    Figure  8.  Impact of different values of $\delta $

    图  9  不同训练集大小的影响

    Figure  9.  Impact of different sizes of training set

    图  10  SCSK单元中卷积核尺寸组合的影响

    Figure  10.  Impact of different combinations of kernel sizes in SCSK unit

    图  11  不同结构的SCSK单元的组成部分

    Figure  11.  Building blocks of different architectures of SCSK unit

    图  12  不同结构的SCSK单元的影响

    Figure  12.  Impact of different architectures of SCSK unit

    图  13  SPUO中使用不同距离参数r的影响

    Figure  13.  Impact of different values of r in SPUO

    图  14  SPUO中判断准则使用Wishart距离和K-Wishart距离的影响

    Figure  14.  Impact of using Wishart distance and K-Wishart distance as criterion in SPUO

    表  1  Flevoland图像分类结果表(%)

    Table  1.   Classification accuracy comparison on Flevoland image (%)

    Method12345678910
    CNN99.3093.3692.2291.5993.5692.7995.7397.9397.5498.59
    PCN92.1195.7698.7196.4991.0796.2797.5296.8796.1392.23
    R5FCN99.8698.3299.3296.2793.0392.1296.0097.4598.1598.39
    SKFCN99.8997.3999.6098.5595.4297.2896.8198.8097.2898.98
    SCSKFCN99.9698.6099.7598.7496.4797.6097.7398.9997.8998.79
    Proposed99.9898.8799.9199.4498.9398.0799.0399.7897.4599.17
    Method1112131415OAKappaTraining (s)Test (s)
    CNN92.2596.6998.8496.7865.4295.1994.76247.73.1
    PCN96.6296.3299.0598.0681.2696.3696.04216.81.2
    R5FCN97.1297.6899.6199.0886.1297.4197.1889.30.9
    SKFCN99.1599.0899.6199.4683.7798.4698.32105.61.0
    SCSKFCN99.4799.4399.6499.7382.6598.8098.70112.41.1
    Proposed99.5699.6899.8999.5090.5199.2499.18(19.1)+132.81.1
    *注:Water(1); Stembeans(2); Forest(3); Potatoes(4); Grasses(5); Beet(6); Rapeseed(7); Peas(8); Lucerne(9); Bare soil(10); Wheat2(11); Wheat1(12); Wheat3(13); Barley(14); Building(15); Proposed表示SCSKFCN-SPUO方法
    下载: 导出CSV

    表  2  Oberpfaffenhofen图像分类结果表(%)

    Table  2.   Classification accuracy comparison on Oberpfaffenhofen image (%)

    ClassCNNARCNR5FCNSKFCNSCSKFCNProposed
    Wood land95.0296.1394.9796.2596.6897.18
    Open areas97.1797.3297.5197.7697.9998.80
    Built-up areas86.4293.9290.4794.2794.6494.86
    OA94.0996.2595.2796.6096.9097.51
    Kappa89.8893.6491.9594.2394.7495.76
    Training (s)225.9189.6120.1137.2159.2(50.3)+173.6
    Test (s)4.61.61.51.71.91.9
    *注:Proposed表示SCSKFCN-SPUO方法
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-11
  • 修回日期:  2021-06-22
  • 网络出版日期:  2021-07-05
  • 刊出日期:  2021-08-28

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