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层次极限学习机用于高光谱图像预测绝缘子污秽度

杨刚 李恒超 谭蓓 石超群 张血琴 郭裕钧 吴广宁

杨刚, 李恒超, 谭蓓, 石超群, 张血琴, 郭裕钧, 吴广宁. 层次极限学习机用于高光谱图像预测绝缘子污秽度[J]. 西南交通大学学报, 2020, 55(3): 579-587. doi: 10.3969/j.issn.0258-2724.20190093
引用本文: 杨刚, 李恒超, 谭蓓, 石超群, 张血琴, 郭裕钧, 吴广宁. 层次极限学习机用于高光谱图像预测绝缘子污秽度[J]. 西南交通大学学报, 2020, 55(3): 579-587. doi: 10.3969/j.issn.0258-2724.20190093
YANG Gang, LI Hengchao, TAN Bei, SHI Chaoqun, ZHANG Xueqin, GUO Yujun, WU Guangning. Application of Hierarchical Extreme Learning Machine in Prediction of Insulator Pollution Degree Using Hyperspectral Images[J]. Journal of Southwest Jiaotong University, 2020, 55(3): 579-587. doi: 10.3969/j.issn.0258-2724.20190093
Citation: YANG Gang, LI Hengchao, TAN Bei, SHI Chaoqun, ZHANG Xueqin, GUO Yujun, WU Guangning. Application of Hierarchical Extreme Learning Machine in Prediction of Insulator Pollution Degree Using Hyperspectral Images[J]. Journal of Southwest Jiaotong University, 2020, 55(3): 579-587. doi: 10.3969/j.issn.0258-2724.20190093

层次极限学习机用于高光谱图像预测绝缘子污秽度

doi: 10.3969/j.issn.0258-2724.20190093
基金项目: 国家自然科学基金(61871335);中央高校前沿交叉基础研究项目(A0920502051814-5)
详细信息
    作者简介:

    杨刚(1993—),男,博士研究生,研究领域为遥感图像智能处理,E-mail:ygfxr@my.swjtu.edu.cn

  • 中图分类号: TM85

Application of Hierarchical Extreme Learning Machine in Prediction of Insulator Pollution Degree Using Hyperspectral Images

  • 摘要: 高光谱图像具有图谱合一、光谱范围广及分辨率高等优势,能精细化地反映物质微观特性. 为此,引入高光谱成像技术以非接触式预测绝缘子污秽度. 考虑到极限学习机具有学习效率高和泛化能力强等优点,提出基于正则化约束极限学习机的绝缘子污秽度预测(extreme learning machine-insulator pollution degree prediction,ELM-IPDP)模型. 此外,为进一步提升预测性能,引入层次极限学习机从复杂的高光谱图像中学习出有效、抽象、判决性特征表示,继而建立基于层次极限学习机的绝缘子污秽度预测(hierarchical ELM-IPDP,HELM-IPDP)模型. 在不同的训练集与测试集比例和不同隐含层神经元个数的情况下分别进行实验,从实验结果可知:ELM-IPDP模型和HELM-IPDP模型的预测性能基本上随着隐含层神经元个数和训练样本的增加而不断提高;当训练集与测试集比例为9∶1时,ELM-IPDP模型的均方根误差和相关系数分别为0.040 3和0.944 7,而HELM-IPDP模型的均方根误差和相关系数分别提升到0.022 3和0.972 0.

     

  • 图 1  HELM模型结构

    Figure 1.  Architecture of the HELM model

    图 2  样本光谱曲线

    Figure 2.  Spectrum curves of samples

    图 3  使用SG + MSC算法得到的光谱曲线

    Figure 3.  Spectrum curves obtained by using Savitzky-Golay and multiplicative scatter correction algorithms

    图 4  不同绝缘子污秽度的平均光谱曲线

    Figure 4.  Average spectrum curves for different insulator pollution degrees

    图 5  ELM-IPDP和HELM-IPDP模型流程

    Figure 5.  Flowchart of ELM-IPDP and HELM-IPDP models

    图 6  不同L值与r、RMSE预测性能

    Figure 6.  Different values of L versus prediction performance of r and RMSE

    表  1  第Ⅱ、Ⅲ、Ⅳ级间的SAD值

    Table  1.   SAD values between the Ⅱ,Ⅲ,and Ⅳ levels

    污秽级第Ⅱ级第Ⅲ级第Ⅳ级
    第Ⅱ级00.103 70.208 4
    第Ⅲ级0.103 700.104 7
    第Ⅳ级0.208 40.104 70
    下载: 导出CSV

    表  2  不同训练集样本量下ELM-IPDP和HELM-IPDP模型的预测结果

    Table  2.   Predicted results of training sets with different train sample sizes for ELM-IPDP and HELM-IPDP models

    比值(训练集组数/
    测试集组数)
    ELM-IPDP模型 HELM-IPDP模型
    eRMSE-TrTeRMSE-PrP eRMSE-TrTeRMSE-PrP
    5∶5 (108/108)0.035 70.909 90.044 20.927 9 0.031 70.919 80.035 40.921 9
    6∶4 (130/86)0.034 60.918 50.044 50.933 0 0.033 70.913 90.033 70.925 1
    7∶3 (151/65)0.034 30.920 60.042 50.932 8 0.030 00.934 60.031 20.934 8
    8∶2 (173/43)0.034 10.921 00.042 90.932 0 0.031 60.927 60.028 50.942 1
    9∶1 (194/22)0.034 40.919 20.040 30.944 7 0.031 00.930 70.022 30.972 0
     注:eRMSE-TeRMSE-P 为训练集和测试集的 RMSE;rTrP 为训练集和测试集的 r.
    下载: 导出CSV
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  • 收稿日期:  2019-03-05
  • 修回日期:  2019-06-25
  • 网络出版日期:  2019-09-04
  • 刊出日期:  2020-06-01

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