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首页> 外文会议>Terahertz Physics, Devices, and Systems IX: Advanced Applications in Industry and Defense >Robust identification of concealed dangerous substances using THz imaging spectroscopy
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Robust identification of concealed dangerous substances using THz imaging spectroscopy

机译：使用THz成像光谱仪对隐藏的危险物质进行可靠的鉴定

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False alarm rates must be kept sufficiently low if a method to detect and identify objects or substances is to be implemented in real life applications. This is also true when trying to detect and identify dangerous substances such as explosives and drugs that are concealed in packaging materials. THz technology may be suited to detect these substances, especially when imaging and spectroscopy are combined. To achieve reasonable throughput, the detection and identification process must be automated and this implies reliance on algorithms to perform this task, rather than human beings. The identification part of the algorithm must compare spectral features of the unknown substance with those in a library of features and determining the distance, in some sense, between these features. If the distance is less than some defined threshold a match is declared, In this paper we consider two types of spectral characteristic that are derived from measured time-domain signals measured in the THz regime: the absorbance and its derivative. Also, we consider two schemes to measure the distance between the unknown and library characteristics: Spectral Angle Mapping (SAM) and Principal Component Analysis (PCA). Finally, the effect of windowing of the measured time-domain signal on the performance of the algorithms is studied, by varying the Blackman-Harris (B-H) window width. Algorithm performance is quantified by studying the receiver-operating characteristics (ROC). For the data considered in this study we conclude that the best performance is obtained when the derivative of the absorbance is used in combination with a narrow B-H window and SAM. SAM is a more straight-forward method and requires no large training data sets and tweaking.
机译：如果要在现实生活中的应用中采用一种检测和识别物体或物质的方法，则虚假警报率必须保持足够低。当试图检测和识别隐藏在包装材料中的危险物质（例如炸药和毒品）时，也是如此。太赫兹技术可能适合检测这些物质，尤其是在将成像和光谱学结合使用时。为了实现合理的吞吐量，检测和识别过程必须自动化，这意味着要依靠算法来执行此任务，而不是依靠人类。该算法的识别部分必须将未知物质的光谱特征与特征库中的光谱特征进行比较，并在某种意义上确定这些特征之间的距离。如果距离小于某个定义的阈值，则声明匹配。在本文中，我们考虑两种光谱特征，它们是从以THz方式测量的时域信号中得出的：吸光度及其导数。此外，我们考虑了两种方案来测量未知特征和库特征之间的距离：谱角映射（SAM）和主成分分析（PCA）。最后，通过改变布莱克曼-哈里斯（Blackman-Harris）（B-H）窗口宽度，研究了所测量的时域信号的加窗对算法性能的影响。通过研究接收机工作特性（ROC）量化算法性能。对于本研究中考虑的数据，我们得出结论，当吸光度的导数与窄B-H窗口和SAM结合使用时，可获得最佳性能。 SAM是一种更简单的方法，不需要大量的训练数据集和调整。

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