CAJ | 학술논문

应用高光谱成像技术，基于光谱主成分信息和图像信息的融合实现名优绿茶不同品牌的鉴别。首先采集6个品牌名优绿茶在380～1023 nm波长范围的512幅光谱图像，然后提取并分析绿茶样本的可见近红外光谱响应特性，结合主成分分析法找到了最能体现这6类样本差异的2个特征波段（545和611 nm ），并从这2个特征波段图像中分别提取12个灰度共生矩阵纹理特征参量包括中值、协方差、同质性、能量、对比度、相关、熵、逆差距、反差、差异性、二阶距和自相关，最后融合这12个纹理特征和三个主成分特征变量得到名优绿茶品牌识别的特征信息，利用LS-SVM建立区分模型，预测集识别率达到了100％，同时采用ROC曲线的评估方法来评估分类模型。结果表明综合应用灰度共生矩阵变量和光谱主成分变量作为 LS-SVM 模型输入可实现对绿茶品牌的鉴别。
응용고광보성상기술，기우광보주성분신식화도상신식적융합실현명우록다불동품패적감별。수선채집6개품패명우록다재380～1023 nm파장범위적512폭광보도상，연후제취병분석록다양본적가견근홍외광보향응특성，결합주성분분석법조도료최능체현저6류양본차이적2개특정파단（545화611 nm ），병종저2개특정파단도상중분별제취12개회도공생구진문리특정삼량포괄중치、협방차、동질성、능량、대비도、상관、적、역차거、반차、차이성、이계거화자상관，최후융합저12개문리특정화삼개주성분특정변량득도명우록다품패식별적특정신식，이용LS-SVM건립구분모형，예측집식별솔체도료100％，동시채용ROC곡선적평고방법래평고분류모형。결과표명종합응용회도공생구진변량화광보주성분변량작위 LS-SVM 모형수입가실현대록다품패적감별。
Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA informa-tion and image information fusion .First 512 spectral images of six brands of famous green tea in the 380~1 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system .Then ,12 gray level co-occurrence matrix (GLCM ) features (i .e .,mean ,covariance ,homogeneity ,energy ,contrast ,correlation ,entropy ,inverse gap ,contrast ,difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image .Final-ly ,integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM .Experimental results showed that discriminating rate was 100% in the prediction set .The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm .Overall results suffi-ciently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea .