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  本文關(guān)鍵詞： 灰度共生矩陣 綠茶 主成分分析 最小二乘支持向量機　出處：《光譜學(xué)與光譜分析》2014年05期 　論文類型：期刊論文

【摘要】：應(yīng)用高光譜成像技術(shù),基于光譜主成分信息和圖像信息的融合實現(xiàn)名優(yōu)綠茶不同品牌的鑒別。首先采集6個品牌名優(yōu)綠茶在380~1 023nm波長范圍的512幅光譜圖像,然后提取并分析綠茶樣本的可見近紅外光譜響應(yīng)特性,結(jié)合主成分分析法找到了最能體現(xiàn)這6類樣本差異的2個特征波段(545和611nm),并從這2個特征波段圖像中分別提取12個灰度共生矩陣紋理特征參量包括中值、協(xié)方差、同質(zhì)性、能量、對比度、相關(guān)、熵、逆差距、反差、差異性、二階距和自相關(guān),最后融合這12個紋理特征和三個主成分特征變量得到名優(yōu)綠茶品牌識別的特征信息,利用LS-SVM建立區(qū)分模型,預(yù)測集識別率達(dá)到了100%,同時采用ROC曲線的評估方法來評估分類模型。結(jié)果表明綜合應(yīng)用灰度共生矩陣變量和光譜主成分變量作為LSSVM模型輸入可實現(xiàn)對綠茶品牌的鑒別。
[Abstract]:Based on the fusion of spectral principal component information and image information, different brands of famous and excellent green tea were identified by hyperspectral imaging technology. Firstly, 512 spectral images of six famous green tea brands were collected in the wavelength range of 380,1023nm. Then we extracted and analyzed the response characteristics of green tea samples by visible and near infrared spectroscopy. Combining principal component analysis (PCA), we find out the two characteristic bands that can best reflect the difference of these six kinds of samples, and extract 12 grayscale co-occurrence matrix texture feature parameters, including median value, covariance, homogeneity, from these two characteristic band images. Energy, contrast, correlation, entropy, inverse gap, contrast, difference, second order distance and autocorrelation. Finally, the 12 texture features and three principal component feature variables are fused to obtain the characteristic information of the famous green tea brand. Using LS-SVM to build a differentiation model, The recognition rate of prediction set is 100 and the classification model is evaluated by ROC curve evaluation method. The results show that green tea brand identification can be realized by using gray co-occurrence matrix variable and spectral principal component variable as LSSVM model input.
【作者單位】： 浙江大學(xué)生物系統(tǒng)工程與食品科學(xué)學(xué)院;華東交通大學(xué)機電工程學(xué)院;
【基金】：國家“十二五”科技計劃課題(2011BAD20B12) 國家高技術(shù)研究與發(fā)展項目(2011AA100705) 中央高�；究蒲袠I(yè)務(wù)費專項資金資助
【分類號】：O433

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本文編號：1529155