Analytical lines play a crucial role in laser-induced breakdown spectroscopy (LIBS) technology. To improve the classification performance of LIBS, an image features assisted line selection (IFALS) method which based on spectral morphology and the characteristics of Harris corners was proposed. With this method, a classification experiment for 24 metamorphic rock samples was conducted with linear discriminant analysis (LDA) algorithm. The result showed that the classification accuracy was increased from 94.38% of the conventional classification model MLS-LDA (Manual line selection-linear discriminant analysis) to 98.54% of IFALS-LDA. Furthermore, the time required for the whole classification process was decreased from 2768.38 s of MLS-LDA to 4.36 s of the proposed method, thus the classification efficiency was greatly improved. In addition, compared with the existing automatic line selection method, the convergence rate of IFALS-LDA is significantly faster than that of ASPI (Automatic spectral peaks identification)-LDA. This study demonstrates that LIBS assisted with the image features in machine vision can promote the analytical performance of LIBS technology.

中文翻译：

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激光诱导击穿光谱中的图像特征辅助线选择方法

分析线在激光诱导击穿光谱 (LIBS) 技术中起着至关重要的作用。为了提高LIBS的分类性能，提出了一种基于光谱形态学和Harris角点特征的图像特征辅助线选择(IFALS)方法。利用该方法，利用线性判别分析(LDA)算法对24个变质岩样品进行了分类实验。结果表明，分类准确率从传统分类模型MLS-LDA（Manual line selection-linear discriminant analysis）的94.38%提高到IFALS-LDA的98.54%。此外，整个分类过程所需的时间从MLS-LDA的2768.38 s降低到所提出方法的4.36 s，从而大大提高了分类效率。此外，与现有的自动选线方法相比，IFALS-LDA的收敛速度明显快于ASPI（自动谱峰识别）-LDA。这项研究表明，LIBS 辅助机器视觉中的图像特征可以提高 LIBS 技术的分析性能。