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首页> 外文期刊>Journal of food engineering >Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling
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Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling

机译：在近红外高光谱成像中融合光谱和纹理信息以改善绿茶分类建模

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摘要

Hyperspectral imaging (HSI) can acquire data in two modes: imaging and spectroscopy, revealing the spatially-resolved spectral properties of materials. Traditional HSI processing in the close-range domain primarily focuses on the spectral information with minimal utilisation of the spatial information present in the data. The present work describes a methodology for utilising the spatial information present in HSI data to improve classification modelling over that achievable with spectral information alone. The methodology has been evaluated using near infrared (NIR) HSI data of sixteen green tea products from seven different countries. The methodology involves selecting and sharpening an image plane to enhance the textural details. The textural information is then extracted from the statistical properties of the grey level co-occurrence matrix (GLCM) of the sharpened image plane using a moving window operation. Finally, the textural properties are combined with the spectral information using one of the three different levels of data fusion, i.e. raw data level, feature level and decision level. Raw data-level fusion involved concatenating the spectral and textural data before performing the classification task. The feature-level fusion involved performing principal component analysis (PCA) on spectral and textural information and combining the PC scores obtained prior to performing classification. Decision-level fusion involved a majority voting scheme to enhance the final classification maps. All the classification tasks were performed using multi-class support vector machine (SVM) models. The results showed that combining the textural and spectral information during modelling resulted in improved classification of the sixteen green tea products compared to models built using spectral or textural information alone.
机译：高光谱成像（HSI）可以以两种模式获取数据：成像和光谱学，揭示了材料在空间上分辨的光谱特性。近距离域中的传统HSI处理主要集中在频谱信息上，而对数据中存在的空间信息的利用却很少。本工作描述了一种方法，该方法可利用HSI数据中存在的空间信息来改善分类建模，而仅凭光谱信息即可实现。使用来自七个不同国家的十六种绿茶产品的近红外（NIR）HSI数据对方法进行了评估。该方法包括选择和锐化图像平面以增强纹理细节。然后使用移动窗口操作从锐化后的图像平面的灰度共生矩阵（GLCM）的统计属性中提取纹理信息。最后，使用三个不同数据融合级别之一，即原始数据级别，特征级别和决策级别，将纹理特性与光谱信息结合起来。原始数据级融合涉及在执行分类任务之前将光谱和纹理数据进行合并。特征级融合涉及对光谱和纹理信息执行主成分分析（PCA），并组合在执行分类之前获得的PC分数。决策级融合涉及多数表决方案，以增强最终分类图。所有分类任务均使用多类支持向量机（SVM）模型执行。结果表明，与仅使用光谱或纹理信息建立的模型相比，在建模过程中结合纹理和光谱信息可改善16种绿茶产品的分类。

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  • 来源
    《Journal of food engineering》 |2019年第5期|40-47|共8页
  • 作者

    Mishra Puneet; Nordon Alison; Asaari Mohd Shahrimie Mohd; Lian Guoping; Redfern Sally;

  • 作者单位

    Univ Strathclyde, Dept Pure & Appl Chem, WestCHEM, Glasgow G1 1XL, Lanark, Scotland|Univ Strathclyde, Ctr Proc Analyt & Control Technol, Glasgow G1 1XL, Lanark, Scotland;

    Univ Strathclyde, Dept Pure & Appl Chem, WestCHEM, Glasgow G1 1XL, Lanark, Scotland|Univ Strathclyde, Ctr Proc Analyt & Control Technol, Glasgow G1 1XL, Lanark, Scotland;

    Univ Antwerp, Dept Phys, Vis Lab, Campus Drie Eiken,Edegemsesteenweg 200-240, B-2610 Antwerp, Belgium;

    Unilever R&D Colworth, Colworth Sci Pk, Bedford MK44 1LQ, England|Univ Surrey, Dept Chem & Proc Engn, Guildford GU2 7XH, Surrey, England;

    Unilever R&D Colworth, Colworth Sci Pk, Bedford MK44 1LQ, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Chemical imaging; Texture; Support vector machine (SVM); Grey level co-occurrence matrix (GLCM); Data fusion; Green tea;

    机译：化学成像;纹理;支持向量机（SVM）;灰度共生矩阵（GLCM）;数据融合;绿茶;

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