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基于协峭度张量的高光谱图像异常检测

孟令博 耿修瑞

引用本文: 孟令博, 耿修瑞. 基于协峭度张量的高光谱图像异常检测[J]. 电子与信息学报, 2019, 41(1): 150-155. doi: 10.11999/JEIT180280 shu
Citation:  Lingbo MENG, Xiurui GENG. A Hyperspectral Imagery Anomaly Detection Algorithm Based on Cokurtosis Tensor[J]. Journal of Electronics and Information Technology, 2019, 41(1): 150-155. doi: 10.11999/JEIT180280 shu

基于协峭度张量的高光谱图像异常检测

    作者简介: 孟令博: 女,1989年生,博士生,研究方向为高光谱图像特征提取及异常检测;
    耿修瑞: 男,1965年生,研究员,研究方向为高光谱图像处理技术
    通讯作者: 耿修瑞,xrgeng@mail.ie.ac.cn
摘要: 高光谱图像中的异常像元往往具有在图像中出现的概率低和游离于背景数据云团之外的特点,如何“自动”确定这些异常像元是高光谱遥感图像处理中的一个重要研究方向。经典的高光谱异常检测方法一般从图像的统计特性入手,广泛应用的RXD异常检测算法通过计算图像的2阶统计特征,可以直接给出异常点的分布情况,算法复杂度低,但缺点是没有考虑到图像的高阶统计信息。基于独立成分分析的异常检测算法虽然考虑了高阶统计量对异常点的敏感性,但需要反复迭代提取异常成分后,再对提取后的成分进行异常检测。该文提出一种基于协峭度张量的异常检测算法,该算法不需要事先提取异常成分,可以直接对观测像元进行逐一检测,从而给出异常点的分布情况。基于模拟数据和真实数据的实验结果表明,该方法能够在检测出异常像元的同时更好地压制背景信息、减小虚警率,从而提高异常检测精度。

English

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    1. [1]

      缪祥华, 单小撤. 基于密集连接卷积神经网络的入侵检测技术研究. 电子与信息学报, 2020, 41(0): 1-7.

  • 图 1  数据在某个方向的偏度值(红色线条长度)和峭度值(蓝色线条长度)

    图 2  模拟数据各波段灰度图

    图 3  异常检测结果灰度图

    图 4  COSD, RXD, COKD, KPCA-RXD算法的ROC曲线

    图 5  真实的高光谱图像检测结果

    图 6  4种异常检测算法的ROC曲线

    表 1  4种异常检测算法的AUC

    算法AUC
    COKD0.9997
    RXD0.9934
    COSD0.9996
    KPCA-RXD0.9936
    下载: 导出CSV

    表 2  4种异常检测算法的AUC

    算法AUC
    COKD0.9832
    RXD0.9779
    COSD0.9830
    KPCA-RXD0.9778
    下载: 导出CSV
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文章相关
  • 通讯作者:  耿修瑞, xrgeng@mail.ie.ac.cn
  • 收稿日期:  2018-03-26
  • 录用日期:  2018-10-18
  • 网络出版日期:  2018-10-24
  • 刊出日期:  2019-01-01
通讯作者: 陈斌, bchen63@163.com
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