基于精调的膨胀编组-交叉CNN的PolSAR地物分类

肖东凌 刘畅

肖东凌, 刘畅. 基于精调的膨胀编组-交叉CNN的PolSAR地物分类[J]. 雷达学报, 2019, 8(4): 479–489. doi: 10.12000/JR19039
引用本文: 肖东凌, 刘畅. 基于精调的膨胀编组-交叉CNN的PolSAR地物分类[J]. 雷达学报, 2019, 8(4): 479–489. doi: 10.12000/JR19039
XIAO Dongling and LIU Chang. PolSAR terrain classification based on fine-tuned dilated group-cross convolution neural network[J]. Journal of Radars, 2019, 8(4): 479–489. doi: 10.12000/JR19039
Citation: XIAO Dongling and LIU Chang. PolSAR terrain classification based on fine-tuned dilated group-cross convolution neural network[J]. Journal of Radars, 2019, 8(4): 479–489. doi: 10.12000/JR19039

基于精调的膨胀编组-交叉CNN的PolSAR地物分类

doi: 10.12000/JR19039
基金项目: 国家自然科学基金(61471340),国家重点研发计划(2017YFB0503001)
详细信息
    作者简介:

    肖东凌(1995–),男,四川眉山人,硕士生,研究方向为SAR图像目标检测识别、PolSAR图像地物分类。E-mail: xdluestc@outlook.com

    刘畅:刘   畅(1978–),男,山东烟台人,研究员,博士生导师。2006年在中国科学院电子学研究所获得博士学位,现担任中国科学院电子学研究所研究员、博士生导师。主要研究方向为SAR系统及其相关SAR成像处理技术等。E-mail: cliu@mail.ie.ac.cn

    通讯作者:

    刘畅 cliu@mail.ie.ac.cn

  • 中图分类号: TN958

PolSAR Terrain Classification Based on Fine-tuned Dilated Group-cross Convolution Neural Network

Funds: The National Natural Science Foundation of China (61471340), The State Key Research Development Program (2017YFB0503001)
More Information
  • 摘要: 在极化合成孔径雷达(PolSAR)地物分类研究中,基于实数CNN的分类算法无法充分利用PolSAR图像丰富的通道相位信息,并且在逐像素预测中存在大量冗余计算,导致分类效率较低。针对以上问题,该文采用一种复数域下的像素映射深度模型,实现低采样率下精确且高效的PolSAR地物分类。为充分使用PolSAR数据的通道相位信息,该文基于一种编组-交叉卷积网络(GC-CNN)将分类模型推广到复数域,并利用网络提取的复数特征及其对应的相位和幅度来实现更精确的分类;为加快分类速度,该文还采用了一种精调的膨胀编组-交叉卷积网络(FDGC-CNN)来实现像素到像素的直接映射,并进一步提升了分类精度。在基于AIRSAR平台的16类地物数据和E-SAR平台的4类地物数据的实验中,该文采用的FDGC-CNN模型相较于SVM分类器和实数CNN模型,能够更准确和更高效地实现多类别地物分类,全局分类精度分别为96.94%和90.07%、总耗时4.22 s和4.02 s。

     

  • 图  1  编组-交叉卷积神经网络结构示意图

    Figure  1.  Structure of group-cross convolution neural network

    图  2  编组-交叉卷积层结构示意图

    Figure  2.  Structure of group-cross convolution layer

    图  4  GC-CNN模型损失计算结构

    Figure  4.  Loss calculation of GC-CNN

    图  3  膨胀编组-交叉卷积神经网络结构示意图

    Figure  3.  Structure of Dilate Group-Cross CNN (DGC-CNN)

    图  5  DGC-CNN 精调流程图

    Figure  5.  Flowchart of DGC-CNN fine-tuning process

    图  6  CNN和GC-CNN参数自由度结构图

    Figure  6.  The DoF of CNN and GC-CNN

    图  7  Flevoland-Netherlands地区L, P, C波段PolSAR数据

    Figure  7.  L, P, C-band PolSAR image data over the Flevoland-Netherlands region

    图  8  GC-CNN和CNN训练loss收敛图

    Figure  8.  Training loss convergence curves of GC-CNN and CNN

    图  9  Flevoland-Netherlands地区分类结果图

    Figure  9.  Classification images of the Flevoland-Netherlands region

    图  10  Oberpfaffenhofen地区L波段PolSAR数据

    Figure  10.  L-band PolSAR image data over the Oberpfaffenhofen region

    图  11  Oberpfaffenhofen地区分类结果图

    Figure  11.  Classification images of the Oberpfaffenhofen

    图  12  Flevoland-Netherlands和Oberpfaffenhofen地区实验各模型评估结果曲线

    Figure  12.  Evaluate results curve of models on Flevoland-Netherlands and Oberpfaffenhofen

    表  1  Flevoland-Netherlands地区PolSAR图像分类结果表

    Table  1.   Classification results of Flevoland-Netherlands region

    Class (%) 1 (2.0) 2 (4.9) 3 (0.9) 4 (0.6) 5 (1.6) 6 (1.5) 7 (1.0) 8 (7.2) 9 (5.2) 10 (5.0)
    Train 600 400 600 600 600 700 700 300 240 100
    Total 29630 8031 66148 90681 38015 46583 68427 4155 4611 1982
    SVM 92.47 97.89 97.52 98.77 95.28 92.46 96.34 96.57 89.42 95.67
    CNN 97.81 99.35 98.99 98.93 99.96 97.05 99.21 96.21 83.65 88.27
    Dilated CNN 97.66 99.48 98.79 98.79 99.91 96.78 98.68 94.12 79.03 84.79
    GC-CNN 96.72 99.60 99.03 99.00 99.96 97.51 99.32 96.59 93.20 95.62
    DGC-CNN 96.71 99.49 98.80 98.99 99.95 97.43 99.17 96.58 91.93 95.05
    FDGC-CNN 96.63 99.29 98.90 98.59 99.95 98.65 99.59 97.11 91.32 97.01
    Class (%) 11 (4.4) 12(4.5) 13(5.4) 14(4.9) 15(4.4) 16(1.1) OA Kappa FWIoU Time (s)
    Train 600 100 100 400 200 400
    Total 13485 2206 1838 8203 4536 42861
    SVM 98.34 93.02 98.89 98.56 28.54 57.68 93.27 92.44 87.04 669.00
    CNN 97.81 96.48 99.93 99.56 86.69 69.36 95.72 95.31 92.77 220.31
    Dilated CNN 97.81 91.10 99.83 99.67 78.99 63.02 95.16 94.34 91.56 3.94
    GC-CNN 97.83 98.05 99.98 99.57 87.04 72.79 96.49 95.92 93.64 401.56
    DGC-CNN 97.86 97.37 99.82 99.45 82.36 70.13 96.14 95.50 93.04 4.32
    FDGC-CNN 96.49 98.85 99.98 99.73 89.61 77.99 96.94 96.44 94.29 4.32
    *注: Grass(1); Flax(2); Potato(3); Wheat(4); Rapseed(5); Beet(6); Barly(7); Peas(8); Maize(9); Bean(10); Fruit(11); Onion(12); Oats(13); Lucerne(14); Building(15); Road(16)
    下载: 导出CSV

    表  2  Oberpfaffenhofen地区PolSAR图像分类结果表

    Table  2.   Classification results of oberpfaffenhofen region

    Class (%) Train Total CNN Dilated CNN GC-CNN DGC-CNN FDGC-CNN
    WoodLand (1.0) 3000 290915 88.14 88.33 90.40 89.67 90.55
    FarmLand (0.5) 3000 622739 94.02 94.23 93.54 94.27 95.30
    Urban (1.1) 3000 274684 82.78 79.93 85.80 82.36 86.03
    Roads (1.8) 2500 137400 78.89 76.45 78.82 76.08 73.65
    OA 88.83 88.13 89.72 88.90 90.07
    Kappa 81.40 79.26 82.80 81.20 83.36
    FWIoU 80.70 80.44 81.72 80.42 82.11
    Time (s) 262.47 2.60 513.43 3.21 4.02
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-04
  • 修回日期:  2019-03-19
  • 网络出版日期:  2019-05-05
  • 刊出日期:  2019-08-28

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