On-site visualized classification of transparent hazards and noxious substances on a water surface by multispectral techniques

Huang Haocai; Liu Shuchang; Wang Chao; Xia Kaibo; Zhang Dejun; Wang Hangzhou; Zhan Shuyue; Huang Hui; He Shuangyan; Liu Caicai; Li Xiaorun

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On-site visualized classification of transparent hazards and noxious substances on a water surface by multispectral techniques

机译：通过多光谱技术在现场可视化分类水面上的透明危害和有毒物质

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This paper investigated the use of spectra and multispectral images for on-site visualized classification of transparent hazards and noxious substances (HNS), such as benzene, xylene, and palm oil, floating on a water surface with the potential use for rapid classification of multiple HNS during a leak accident. Partial least-squares discrimination analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models achieved a classification accuracy of 100% for spectral reflectance (325-900 nm) and multispectral image at nine wavelengths. Wavelength division and selection were applied for spectra and spectral images, respectively, to reduce the difficulty in data collection and to simplify the redundant bands. This was followed by PLS-DA and LS-SVM modeling. The LS-SVM model based on the least wavelengths (365, 410, 450, and 850 nm) of multispectral images was suggested as the most effective method for on-site visualized classification of transparent HNS on a water surface. (C) 2019 Optical Society of America

机译：本文研究了光谱和多光谱图像的现场可视化分类透明危害和有毒物质（HNS），如苯，二甲苯和棕榈油，漂浮在水面上，潜在使用用于多重的快速分类在泄漏事故中的HNS。局部最小二乘辨别分析（PLS-DA）和最小二乘支持向量机（LS-SVM）模型实现了九个波长的光谱反射率（325-900nm）和多光谱图像的100％的分类精度。波长划分和选择分别应用于光谱和光谱图像，以减少数据收集中的难度并简化冗余频带。这是PLS-DA和LS-SVM建模。基于多光谱图像的基于最小波长（365,410,450和850nm）的LS-SVM模型被建议作为在水面上现场可视化分类的最有效方法。（c）2019年光学学会

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来源
《Applied optics》 |2019年第16期|共9页
作者
Huang Haocai; Liu Shuchang; Wang Chao; Xia Kaibo; Zhang Dejun; Wang Hangzhou; Zhan Shuyue; Huang Hui; He Shuangyan; Liu Caicai; Li Xiaorun;
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作者单位

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Technol Coll Sci Hangzhou 310014 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

East China Sea Environm Monitoring Ctr Shanghai 310058 Peoples R China;

Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China;

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正文语种 eng
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On-site visualized classification of transparent hazards and noxious substances on a water surface by multispectral techniques

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