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基于离散余弦变换和支持向量机的多光谱纹理图像的茶叶分类研究

期刊名称：光谱学与光谱分析. 2009,29(5)?: 1382-1385
时间：0
分类：TP312[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] TP317.4[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
作者机构：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310029, [2]厦门大学物理系,福建厦门361005
相关基金：国家高技术研究发展计划（“863”计划）项目（2007AA102210）和国家自然科学基金项目（30671213,30600371）资助
相关项目：基于机器视觉及近红外光谱技术的茶叶品质无损检测方法研究

关键词：多光谱成像仪, 茶叶, 纹理特征, 离散余弦变换, 支持向量机, Multi-spectral image, Tea, Texture feature, Discrete cosine transform, Least squares-support vector machine

中文摘要：

提出一种利用多光谱图像纹理特征进行茶叶分类的新方法。图像由MS3100-3CCD光谱成像仪获得,光谱成像仪提供近红外（NIR）、红色（R）和绿色（G）的3个波段的图像。首先对原图像的NIR波段图像提取均方值,然后应用离散余弦变换算法,构造出8个带通和高通滤波器对NIR通道的图像进行滤波并提取均方差值,最后应用支持向量机技术,分别对原图像的NIR提取的均方差值和用8个滤波器滤过的图像提取的均方差值进行建模。茶叶样本总共为240个,训练和预测各为120个,每种训练样本和预测样本各为20个。结果表明经过8个滤波器处理图像的识别率为100%,而没有经过滤波处理的纹理图像识别率只有73.33%,说明离散余弦变换算法设计的滤波器是一种非常有效的纹理识别技术,此实验同时也为茶叶的分类提供一种快速和无损的新方法。

英文摘要：

Based on multispectral digital image texture feature, a new rapid and nondestructive method for discriminating tea categories was put forward. The new method combines the advantages of DCT （discrete cosine transform） and least squares-support vector machine （LS-SVM）. In the present study, the images for each sample were captured using a red （R） waveband, near infrared （NIR） waveband and green （G） waveband multispectral digital imager. The three wavebands of image can be combined into one image, which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. Three images for one sample can be obtained simultaneously. Eighty filters were designed based on DCT. One hundred twenty images （twenty for each category） were used for calibration set and one hundred twenty mages （twenty for each category） were used as the prediction. Finally, tea category was classified by LS-SVM. The classification rate using Sd of NIR image was only 73.33%, while it reached 100% using 8 filtered images. The overall results show that the technique combining DCT and SVM can be efficiently utilized for texture recognition of multispectral image, and it also is an effective and simple discrimination way for the tea categories. The whole process is simple and easy to operate, and can be transferred to the industrial world for on-line application.

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