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基于样本类别确定度的半监督分类

高飞 朱福利

高飞, 朱福利. 基于样本类别确定度的半监督分类[J]. 北京航空航天大学学报, 2018, 44(9): 1941-1951. doi: 10.13700/j.bh.1001-5965.2017.0708
引用本文: 高飞, 朱福利. 基于样本类别确定度的半监督分类[J]. 北京航空航天大学学报, 2018, 44(9): 1941-1951. doi: 10.13700/j.bh.1001-5965.2017.0708
GAO Fei, ZHU Fuli. Semi-supervised classification based on class certainty of samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1941-1951. doi: 10.13700/j.bh.1001-5965.2017.0708(in Chinese)
Citation: GAO Fei, ZHU Fuli. Semi-supervised classification based on class certainty of samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1941-1951. doi: 10.13700/j.bh.1001-5965.2017.0708(in Chinese)

基于样本类别确定度的半监督分类

doi: 10.13700/j.bh.1001-5965.2017.0708
基金项目: 

国家自然科学基金 61771027

详细信息
    作者简介:

    高飞  男, 博士, 教授, 硕士生导师。主要研究方向:数字图像融合与处理、运动目标检测、机器学习

    朱福利  男, 硕士研究生。主要研究方向:机器学习

    通讯作者:

    高飞, E-mail: feigao2000@163.com

  • 中图分类号: V221+.3;TB553

Semi-supervised classification based on class certainty of samples

Funds: 

National Natural Science Foundation of China 61771027

More Information
  • 摘要:

    在对遥感图像进行分类时,全监督算法往往需要足够的标记样本进行训练,然而标记的过程是耗时和昂贵的,相反收集大量的无标记样本是很容易的。为了在学习过程中能够有效利用未标记样本的信息,本文提出了基于样本类别确定度(CCS)的半监督分类算法。首先,利用多分类支持向量机(SVM)得到未标记样本属于各类别的确定度,有效地衡量了未标记样本类别可靠性;其次,对样本类别确定度进行预处理,提升利用未标记样本的安全性;最后,基于样本类别确定度设计了半监督线性判别分析(LDA)降维算法并对其进行核化,使得样本在降维后的子空间更具有可分性,并根据降维后的数据特点,采用最近邻分类器对新样本进行分类。利用真实的合成孔径雷达(SAR)图像进行测试,验证了在标记样本较少的情况下,本文算法在性能上优于全监督和其他半监督算法,并能够快速收敛。

     

  • 图 1  本文算法流程

    Figure 1.  Flowchart of proposed algorithm

    图 2  多分类SVM构造及未标记样本初始类别确定度获取

    Figure 2.  Multi-class SVM construction and acquisition of initial class certainty of unlabeled sampless

    图 3  3种训练目标的光学图像

    Figure 3.  Optical images of three kinds of training objective

    图 4  3种训练目标的SAR图像

    Figure 4.  SAR images of three kinds of training objective

    图 5  不同降维算法对本文算法分类精度影响走势

    Figure 5.  Classification accuracy trend of proposed algorithm with different dimension reduction algorithms

    图 6  不同阈值设置时本文算法的分类精度走势

    Figure 6.  Classification accuracy trend of proposed algorithm with different threshold setting

    图 7  3类标记坦克样本在LDA和半监督LDA中的二维特征

    Figure 7.  Two-dimensional feature of three kinds of labeled tank samples in LDA and semi-supervised LDA

    图 8  全监督算法与本文算法的分类精度走势

    Figure 8.  Classification accuracy trend of supervised algorithm and proposed algorithm

    图 9  不同半监督算法的分类精度走势

    Figure 9.  Classification accuracy trend of different semi-supervised algorithms

    表  1  训练样本与测试样本的种类及样本数量

    Table  1.   Types and quantities of training samples and testing samples

    样本种类 训练样本 测试样本
    T72
    (sn_132)
    BMP2
    (sn_c21)
    BTR70
    (sn_c71)
    T72
    (sn_s7)
    BMP2
    (sn_c9566)
    BTR70
    (sn_c70)
    样本数/幅 232 232 232 191 191 191
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出版历程
  • 收稿日期:  2017-11-13
  • 录用日期:  2017-12-08
  • 刊出日期:  2018-09-20

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