融合视觉注意机制的高光谱RX异常检测算法

李茗欣, 黄远程, 竞霞, 史孟琦

李茗欣, 黄远程, 竞霞, 史孟琦. 融合视觉注意机制的高光谱RX异常检测算法[J]. 红外技术, 2023, 45(4): 402-409.
引用本文: 李茗欣, 黄远程, 竞霞, 史孟琦. 融合视觉注意机制的高光谱RX异常检测算法[J]. 红外技术, 2023, 45(4): 402-409.
LI Mingxin, HUANG Yuancheng, JING Xia, SHI Mengqi. Hyperspectral RX Anomaly Detection Algorithm with Visual Attention Mechanism[J]. Infrared Technology , 2023, 45(4): 402-409.
Citation: LI Mingxin, HUANG Yuancheng, JING Xia, SHI Mengqi. Hyperspectral RX Anomaly Detection Algorithm with Visual Attention Mechanism[J]. Infrared Technology , 2023, 45(4): 402-409.

融合视觉注意机制的高光谱RX异常检测算法

基金项目: 

国家自然科学基金 42171394

痕迹科学与技术公安部重点实验室开放基金 2020FMKFKT07

详细信息
    作者简介:

    李茗欣(1998-),女,陕西西安人,硕士研究生,主要研究方向为高光谱图像异常检测,E-mail:q7461_lmx@163.com

    通讯作者:

    黄远程(1983-),男,湖南郴州人,博士,讲师,硕士生导师,主要研究方向为高光谱图像处理与模式识别。E-mail:yuanchenghuang@xust.edu.cn

  • 中图分类号: TP391.41

Hyperspectral RX Anomaly Detection Algorithm with Visual Attention Mechanism

  • 摘要: 视觉注意机制具有快速引导关注到重点区域的特性,将其引入高光谱图像异常检测中具有可行性。本文从采样方式、波段选取、融入局部光谱特征3方面构建更适用于计算高光谱图像显著性的视觉注意机制模型。针对经典的基于高斯统计分布假设的RX异常检测算法在背景参数估计中易受潜在异常干扰的问题,利用视觉显著性结果对原图像进行高斯加权,在加权后图像中进行背景均值与协方差的重新估算,进而使用更精确的背景参数对原图像进行RX异常检测。在5个经典数据上的实验结果表明,本文方法有效地表现了潜在的异常目标,改进的RX异常检测算法具有更高的检测精度与更低的虚警率。
    Abstract: A visual attention mechanism (VAM) can quickly highlight region-of-interest targets; therefore, it is reasonable to introduce visual attention into hyperspectral image (HSI) anomaly detection tasks. By adjusting a bottom-up VAM model in three aspects, namely sampling method, band selection, and local spectral features, a more applicable VAM model for calculating the saliency of hyperspectral images was constructed. The resulting VAM is called bottom-up hyperspectral saliency map (BUHS). To solve the problem of background parameter estimation in the RX(Reed-Xiaoli) algorithm, which is susceptible to interference, BUHS was used as a Gaussian weighting parameter for the original image, in which new parameters of the RX anomaly method were calculated. Compared to the traditional RX, the background parameters are more accurate. The experimental results on five HSI datasets show that the proposed method can effectively identify potential anomaly targets and improve the RX algorithm with a higher detection accuracy and lower false alarm rate.
  • 图  1   融合视觉注意机制的高光谱RX异常检测算法流程

    Figure  1.   Hyperspectral RX anomaly detection algorithm with visual attention mechanism flowchart

    图  2   高光谱影像与其对应异常目标分布

    Figure  2.   Hyperspectral images and their anomaly targets distribution

    图  3   显著性结果与异常检测结果

    Figure  3.   Saliency results and anomaly detection results

    图  4   ROC曲线

    Figure  4.   ROC curves

    表  1   LCVF模拟异常目标

    Table  1   LCVF simulates anomalous targets

    Column Number of targets/cells Proportion of target Proportion of background
    1 5/20 0.8 0.2
    2 5/20 0.6 0.4
    3 5/5 0.4 0.6
    4 6/6 0.2 0.8
    下载: 导出CSV

    表  2   异常检测数据介绍

    Table  2   Anomaly detection data introduction

    Data Sensor Resolution/m Size Targert type Number of targets/cells
    LCVF AVIRIS 20 120×160×190 Simulated anomalous targets 21/51
    airport4 AVIRIS 3.4 100×100×205 Airport 3/60
    beach4 ROSIS-03 1.3 150×150×102 Car and bare soil 7/68
    urban1 AVIRIS 17.2 100×100×204 Building 9/67
    urban3 AVIRIS 3.5 100×100×191 Ship 11/52
    下载: 导出CSV

    表  3   参数实验结果

    Table  3   Parameters experimental results

    Parameter Size AUC Parameter Size AUC
    Number of bands 3 0.9759
    Number of pyramid layers 3 0.9081 6 0.9621
    5 0.9759 9 0.9681
    7 0.9709 12 0.9777
    9 0.9624 15 0.9779
    18 0.9772
    Spectral feature window size 3×3 0.9769 21 0.9765
    5×5 0.9759 24 0.9746
    7×7 0.975 27 0.9726
    30 0.9725
    下载: 导出CSV

    表  4   消融实验

    Table  4   Ablation experiment

    model Sampling mode Band selection Spectral feature AUC
    A 0.9625
    B 0.9690
    C 0.9788
    D 0.9759
    E 0.9846
    F 0.9831
    G 0.9862
    下载: 导出CSV

    表  5   AUC统计结果

    Table  5   AUC statistics

    RX ITTIRX HITTIRX
    LCVF 0.8333 0.8753 0.9999
    Airport4 0.9526 0.8768 0.9894
    Beach4 0.9588 0.9302 0.9847
    Urban1 0.9907 0.9881 0.9983
    Urban3 0.9513 0.9625 0.9862
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-01
  • 修回日期:  2022-09-06
  • 刊出日期:  2023-04-19

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