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高光谱技术的羊肉品种多波段识别研究
引用本文:王松磊,吴龙国,马天兰,陈亚斌,何建国,贺晓光,康宁波.高光谱技术的羊肉品种多波段识别研究[J].光谱学与光谱分析,2016(9):2937-2945.
作者姓名:王松磊  吴龙国  马天兰  陈亚斌  何建国  贺晓光  康宁波
作者单位:1. 宁夏大学土木与水利工程学院,宁夏 银川 750021; 宁夏大学农学院,宁夏 银川 750021;2. 宁夏大学土木与水利工程学院,宁夏 银川,750021;3. 宁夏大学农学院,宁夏 银川,750021
基金项目:国家自然科学基金青年项目(31101306)
摘    要:利用可见/近红外(400~1 000nm)及近红外(900~1 700nm)高光谱成像技术对宁夏地区滩寒杂交、盐池滩羊、小尾寒羊三个品种羊肉进行识别研究。针对不同波段光谱特点,分别优选出Baseline及SG卷积平滑光谱预处理方法,并运用连续投影算法(SPA)提取特征波长,结合线性判别分析(LDA)及径向基核函数支持向量机(RBFSVM)模型进行全波段及特征波长识别分析。结果表明不同波段高光谱对羊肉品种识别均获得较好效果,其中400~1 000nm波段采用Baseline-Fullwave-RBFSVM及12个特征波长下准确率为100%与98.75%,900~1 700nm波段采用Baseline-Fullwave-RBFSVM及7个特征波长下准确率为96.25%与87.80%;RBFSVM非线性分类准确率高于LDA线性判别结果,400~1 000nm波段识别准确率优于900~1 700nm波段,说明三种羊肉在色泽纹理上差异比成分含量显著,利用高光谱成像技术结合RBFSVM方法能够获得较优的羊肉品种识别效果。

关 键 词:高光谱成像技术  羊肉品种  多波段识别  特征波长  支持向量机

Study on Multi-Bands Recognition for Varieties of Mutton by Using Hyperspectral Technologies
Abstract:This paper focused on the research on identifying and classifying for mutton varieties of Tan-han hybrid sheep,Yanchi Tan-sheep and small-tailed sheep in Ningxia by using visible/near-infrared (400~1 000 nm).Near infrared (900~1 700 nm) hyperspectral technologies,baseline and SG convolution smoothing spectra pretreatment methods were applied respectively ac-cording to the characteristics of different spectrum bands;the characteristic wavelengths were extracted by using successive pro-j ection algorithm (SPA);then combined with linear discriminant analysis (LDA)and radial basis kernel function of support vec-tor machine (RBFSVM)model were applied to identify the different mutton varieties under characteristic wavelengths and full-wave bands.Results showed that there were good effects for mutton varieties identification in different hyperspectral bands, among which Baseline-Fullwave-RBFSVM and the same models under 12 characteristic wavelengths obtained accuracy of 100%and 98.75% in 400~1 000 nm respectively,and Baseline-Fullwave-RBFSVM and the same models under 7 characteristic wave-lengths obtained accuracy of 96.25% and 87.80% in 900~1 700 nm respectively.The identification accuracy of RBFSVM non-linear classification was higher than the LDA linear discriminant result,meanwhile the identification accuracy in 400~1 000 nm bands was better than in 1 000~1 700 nm bands,which explained that the differences of color and texture were more significant than the component contents among the 3 varieties mutton.Combined hyperspectral technologies with RBFSVM models can ob-tain a better recognition effect of mutton varieties.
Keywords:Hyperspectral imaging technology  Varieties of mutton  Multichannel recognition  Characteristic wavelengths  SVM (support vector machine)
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