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基于最优误差自校正极限学习机的高频地波雷达RD谱图海面目标检测算法

张万栋 李庆忠 黎明 武庆明

张万栋, 李庆忠, 黎明, 武庆明.基于最优误差自校正极限学习机的高频地波雷达RD谱图海面目标检测算法.自动化学报, 2021, 47(1): 108−120 doi: 10.16383/j.aas.c180210
引用本文: 张万栋, 李庆忠, 黎明, 武庆明.基于最优误差自校正极限学习机的高频地波雷达RD谱图海面目标检测算法.自动化学报, 2021, 47(1): 108−120 doi: 10.16383/j.aas.c180210
Zhang Wan-Dong, Li Qing-Zhong, Li Ming, Q. M. Jonathan Wu. Sea surface target detection for RD images of HFSWR based on optimized error self-adjustment extreme learning machine. Acta Automatica Sinica, 2021, 47(1): 108−120 doi: 10.16383/j.aas.c180210
Citation: Zhang Wan-Dong, Li Qing-Zhong, Li Ming, Q. M. Jonathan Wu. Sea surface target detection for RD images of HFSWR based on optimized error self-adjustment extreme learning machine. Acta Automatica Sinica, 2021, 47(1): 108−120 doi: 10.16383/j.aas.c180210

基于最优误差自校正极限学习机的高频地波雷达RD谱图海面目标检测算法

doi: 10.16383/j.aas.c180210
基金项目: 

国家重点研发计划 2017YFC1405202

海洋公益性行业科研专项 201605002

国家自然科学基金 61132005

详细信息
    作者简介:

    张万栋  加拿大温莎大学电子与计算机工程系博士研究生. 2018年于中国海洋大学获得硕士学位.主要研究方向为图像处理与模式识别.E-mail: zhang1lq@uwindsor.ca

    黎明  中国海洋大学工程学院教授. 1997年和2003年获得东北大学硕士和博士学位.主要研究方向为智能信息处理与智能控制, 海洋仪器, 海洋能发电系统控制. E-mail: limingneu@ouc.edu.cn

    武庆明  加拿大首席科学家, 加拿大温莎大学教授. 1990年获得英国威尔士大学工学博士学位.主要研究方向为计算机和机器人视觉, 图像处理与模式识别. E-mail:jwu@uwindsor.ca

    通讯作者:

    李庆忠  中国海洋大学工程学院教授. 2000年获得中国农业大学获得博士学位.主要研究方向为信号处理, 图像处理, 模式识别.本文通信作者. E-mail: liqingzhong@ouc.edu.cn

  • 本文责任编委 黄庆明

Sea Surface Target Detection for RD Images of HFSWR Based on Optimized Error Self-adjustment Extreme Learning Machine

Funds: 

National Key Research and Development Program of China 2017YFC1405202

Special Fund for MariaScientific Research in the Public Interest 201605002

National Natural Science Foundation of China 61132005

More Information
    Author Bio:

    ZHANG Wan-Dong  Ph. D. candidate in the Department of Electrical and Computer Engineering, University of Windsor, Canada. He received his master degree from Ocean University of China in 2018. His research interest covers image processing and pattern recognition

    LI Ming  Professor at Ocean University of China. He received his master and Ph. D. degrees both from Northeastern University, China in 1997 and 2003, respectively. His research interest covers intelligent information processing and intelligent control, marine instruments, control of marine power generation systems

    Q. M. Jonathan Wu   Tier 1 Canada Research Chair of Automotive Sensors and Information Systems. Professor at University of Windsor, Canada. He received his Ph. D. degrees from University of Wales, UK in 1990. His research interest covers computer vision, image processing, and pattern recognition

    Corresponding author: LI Qing-Zhong  Professor at Ocean University of China. He received his Ph. D. degrees from China Agricultural University in 2000. His research interest covers signal processing, image processing and pattern recognition. Corresponding author of this paper
  • Recommended by Associate Editor HUANG Qing-Ming
  • 摘要: 高频地波雷达(High-frequency surface wave radar, HFSWR)在超视距舰船目标检测跟踪中有广泛应用.然而, HFSWR工作频段的电磁环境十分复杂, 舰船目标信号往往被淹没在各种噪声中.本文提出一种基于最优误差自校正极限学习机(Optimized error self-adjustment extreme learning machine, OES-ELM)的HFSWR海面目标识别算法.该算法利用二级级联分类策略, 可以显著提高目标的检测效率.首先利用灰度特征和线性分类器快速找出目标的潜在区域.然后利用Haar-like特征和OES-ELM分类器进一步辨识目标和海杂波.在OES-ELM中, 首先利用$L_{1/2}$正则算子裁剪隐层中的"微弱"神经元, 以得到隐层神经元的最优个数; 其次, 通过网络误差回传至隐含层使网络的隐层权值和输出层权值迭代更新至最优状态.实验结果表明:和标准ELM相比, 提出的OES-ELM网络具有更好的性能; 此外, 基于OES-ELM的HFSWR目标检测方法具有良好的实时性和目标检测性能.
    Recommended by Associate Editor HUANG Qing-Ming
    1)  本文责任编委 黄庆明
  • 图  1  经典RD谱图

    Fig.  1  A typical RD image

    图  2  算法总体框架

    Fig.  2  The framework of the proposed method

    图  3  线性分类器的权重误差$e$, $S^+$, $ {T^–}-{S^-}$和灰度级间关系曲线

    Fig.  3  The weighted error $e$, $S^+$ and $ {T^–}-{S^-}$, when training linear classifier on RD image data set, where the x-axis show the different gray-value

    图  4  Haar-like特征

    Fig.  4  Haar-like feature descriptor

    图  5  滑动窗口的选取

    Fig.  5  The chosen of reference window

    图  6  系数比和隐层神经元数目间关系曲线

    Fig.  6  Performance of ratio of the first $l$ accumulation coefficients to the sum coefficients

    图  7  训练误差和迭代次数关系曲线

    Fig.  7  performance of training accuracy with respect to iteration $n$

    图  8  本文算法目标点检测结果

    Fig.  8  The final detection result of our proposed method

    表  1  分类数据集的具体信息

    Table  1  The detail of classification datasets

    数据集 属性数 训练集 测试集
    Hill-Valley 101 606 606
    Iris 4 60 90
    BCW(Original) 10 300 399
    Covtype.binary 54 300 000 281 012
    Wine 13 90 88
    Gisette 5 000 6 000 1 000
    Leukemia 7 129 38 34
    下载: 导出CSV

    表  2  回归数据集的具体信息

    Table  2  The detail of regression datasets

    数据集 属性数 训练集 测试集
    Forest Fires 13 239 278
    Wine Quality 12 2 898 2 000
    Abalone 8 3 000 1 477
    CPUsmall 12 5 000 4 192
    Facebook Metrics 9 300 200
    下载: 导出CSV

    表  3  不同网络在不同数据集下性能对比

    Table  3  Generalization performance comparision

    数据集 方法 Tr_acc Te_acc 神经元
    Hill-Valley ELM 81.36 % 79.44 % 300
    ES-ELM 81.36 % 98.94 % 4 (2m)
    OES-ELM($ L_1 $) 98.63 % 97.57 % 189
    OES-ELM 97.36 % 98.66 % 184
    Iris ELM 92.02 % 82.59 % 30
    ES-ELM 89.16 % 89.17 % 6(2m)
    OES-ELM($ L_1 $) 92.23 % 89.16 % 23
    OES-ELM 91.79 % 90.27 % 23
    BCW (Original) ELM 92.66 % 87.41 % 300
    ES-ELM 98.67 % 98.50 % 4(2m)
    OES-ELM($ L_1 $) 97.01 % 96.47 % 137
    OES-ELM 98.37 % 98.96 % 123
    Covtype. binary ELM 77.29% 79.28 % 500
    ES-ELM 79.83 % 78.15 % 14(2m)
    OES-ELM($ L_1 $) 76.11 % 78.27 % 867
    OES-ELM 79.94 % 78.41 % 899
    Wine ELM 98.77 % 84.88 % 300
    ES-ELM 99.44 % 98.86 % 6(2m)
    OES-ELM($ L_1 $) 95.59 % 98.40 % 42
    OES-ELM 95.61 % 98.91 % 40
    Gisette ELM 92.20 % 86.49 % 5 000
    ES-ELM 95.68 % 84.77 % 4(2m)
    OES-ELM($ L_1 $) 97.99 % 96.09 % 1 355
    OES-ELM 98.01 % 96.74 % 1 205
    Leukemia ELM 71.60 % 74.36 % 5 000
    ES-ELM 84.10 % 69.91 % 4(2m)
    OES-ELM($ L_1 $) 94.12 % 86.91 % 34
    OES-ELM 93.89 % 87.14 % 34
    下载: 导出CSV

    表  4  不同网络在不同数据集下性能对比

    Table  4  Generalization performance comparision

    数据集 方法 $ Tr\_RMSE $ $ Te\_RMSE $ 神经元
    Forest Fires ELM 0.1568 0.1958 200
    OES-ELM($ L_1 $) 0.1467 0.1365 163
    OES-ELM 0.1480 0.1374 161
    Wine Quality ELM 0.2547 0.1799 200
    OES-ELM($ L_1 $) 0.1863 0.1977 244
    OES-ELM 0.1845 0.1921 168
    Abalone ELM 0.0412 0.0816 200
    OES-ELM($ L_1 $) 0.0601 0.0659 108
    OES-ELM 0.0592 0.0647 115
    CPUsmall ELM 0.2550 0.2497 500
    OES-ELM($ L_1 $) 0.2235 0.2226 163
    OES-ELM 0.2021 0.2217 159
    Facebook Metrics ELM 0.3659 0.2185 200
    OES-ELM($ L_1 $) 0.0350 0.0459 27
    OES-ELM 0.0417 0.0458 27
    下载: 导出CSV

    表  5  ELM和OES-ELM在不同正则系数下$ Te\_RMSE $比较

    Table  5  The comparision of ELM and OES-ELM with respect to $ Te\_RMSE $

    C Forest Fires Wine Quality Abalone CPUsmall Facebook Metrics
    ELM OES-ELM ELM OES-ELM ELM OES-ELM ELM OES-ELM ELM OES-ELM
    $ C = 2^{-2} $ 0.1920 0.1286 0.1534 0.2015 0.1065 0.0742 0.2338 0.2285 0.0956 0.0542
    $ C = 2^{0} $ 0.1957 0.1204 0.2148 0.2159 0.1895 0.0638 0.2398 0.2227 0.1386 0.0499
    $ C = 2^{2} $ 0.1491 0.1245 0.1649 0.1958 0.1099 0.0626 0.2345 0.2218 0.1477 0.0612
    $ C = 2^{4} $ 0.2048 0.1367 0.2493 0.2226 0.2201 0.0628 0.2561 0.2214 0.1602 0.0418
    下载: 导出CSV

    表  6  两个数据集的详细信息

    Table  6  The detail of two designed datasets

    数据集 样本数 输入 输出
    维数 特征 维数 类别
    $ X_1 $ 1 274 1 灰度值 2 是否背景
    $ X_2 $ 576 49 Haar-like 2 是否目标
    下载: 导出CSV

    表  7  三种算法的性能对比(时间:平均测试时间(秒))

    Table  7  The performance of These three algorithms (Time: Average testing time (second))

    方法 $ P_d $ $ P_f $ $ M_r $ $ E_r $ 时间
    OES-ELM 92 % 6 % 8 % 14 % 3.65
    改进CFAR 85 % 13 % 15 % 28 % 4.90
    自适应小波 90 % 8 % 10 % 18 % 6.14
    下载: 导出CSV
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  • 收稿日期:  2018-04-12
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