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孙俊,金夏明,毛罕平,武小红,杨宁.高光谱图像技术在掺假大米检测中的应用[J].农业工程学报,):301-307.DOI:doi：10.3969/j.issn.14.21.036
高光谱图像技术在掺假大米检测中的应用
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基金项目:国家自然科学基金资助项目（）；江苏高校优势学科建设工程资助项目PAPD（苏政办发2011 6号）；农业部农业信息服务技术重点实验室课题（2014-AIST-03）；江苏省自然科学基金项目（BK）。
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中文摘要:为了有效判别出优质大米中是否掺入劣质大米，该文研究了一种针对大米掺假问题的快速、无损检测方法。从市场上购买了东北长粒香大米和江苏溧水大米，按纯东北长粒香大米、3∶1、2∶2、1∶3和纯江苏溧水大米共5个掺合水平进行大米试验样本的制备。利用可见-近红外高光谱图像采集系统（390～1050 nm）获取了200个大米样本的高光谱图像。采用ENVI软件确定高光谱图像的感兴趣区域（region of interest, ROI），并提取出所有样本在ROI内的平均高光谱数据。采用支持向量机（support vector machine，SVM）建立全光谱波段下的大米掺假判别模型，径向基（radial basis function，RBF）核函数模型交叉验证准确率为93%、预测集正确率为98%。由于高光谱信息量大、冗余性强且受噪声的影响较大，该文采用主成分分析方法（principal component analysis，PCA）分别对大米高光谱图像和高光谱数据进行处理，从特征选择和特征提取2个角度对原始高光谱数据进行处理，通过主成分权重系数图选择了531.1、702.7、714.3、724.7、888.2和930.6 nm 6个特征波长，通过留一交叉验证法（leave-one-out cross-validation，LOOCV）确定并提取出PCA降维后的最优主成分数（number of principal component，PCs）为9。最后分别将优选出的特征波长和提取出的最优主成分数作为模型的输入，建立SVM模型。试验结果表明，基于特征波长SVM模型的交叉验证准确率为95%、预测集正确率为96%，基于最优主成分数SVM模型的交叉验证准确率为94%、预测集正确率为98%。该研究结果表明，该文建立的基于特征波长和基于最优主成分数的SVM模型均具有较优的预测性能，且利用高光谱图像技术对大米掺假问题进行检测是可行的。
Sun Jun,Jin Xiaming,Mao Hanping,Wu Xiaohong,Yang Ning.Application of hyperspectral imaging technology for detecting adulterate rice[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),):301-307.DOI:doi：10.3969/j.issn.14.21.036
Application of hyperspectral imaging technology for detecting adulterate rice
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Abstract: Rice is an important food ration of Chinese people, which contains a great number of starch, protein, fat and some nutrient elements. However, rice adulteration is becoming one of the most urgent problems and it needs to be solved as soon as possible in Chinese rice market. Therefore, the purpose of this study was to develop a rapid, precise and nondestructive method to detect the rice adulteration. In this paper, some expensive rice with high quality (Chang-Li-Xiang) and some cheap rice with relatively low quality (Li-Shui) were purchased from the local Wal-Mart in Zhenjiang province, China. Then they were mixed together in five different proportions (0:4, 1:3, 2:2, 3:1 and 4:0) by using electronic scale and the sample of rice adulteration were obtained. The visible and near infrared (VIS-NIR) hyperpectral imaging system with the spectral range of 390-1050 nm was used to capture the hyperspectral images of 200 rice samples. ENVI software was adopted to determine the region of interest (ROI) in the hyperspectral image and extract the hyperspectral data by averaging the reflectance from all the pixels in the ROI images. Then the discriminative model for rice adulteration was established by using support vector machine (SVM) and the extracted hyperspectral data in the full spectral range. The performance of the SVM model was evaluated by using the indexes of cross validation accuracy and prediction accuracy. Finally, the cross validation accuracy was 93% and the prediction accuracy was 98% in the full-spectral-SVM. As there were a large number of noise and redundant information in the raw hyperspectral images and hyperspectral data, some data processing methods should be used to remove the noise, accelerate the processing efficiency and improve the performance of the models. In this paper, the traditional principal component analysis (PCA) method was respectively used to process the hyperspectral images and hyperspectral data from the two aspects of feature selection and feature extraction. For the aspect of feature selection, a total of six characteristic wavelengths (531.1, 702.7, 714.3, 724.7, 888.2 and 930.6 nm) were picked up according to the weight coefficient distribution curve of the first four principal component images under the full wavelengths. For the aspect of feature extraction, the optimal number of principal component (PCs) was determined as 9 by using the leave-one-out cross-validation (LOOCV). Finally, the two kinds of simplified SVM models were respectively developed by using the input data at the six characteristic wavelengths and at the optimal PCs. The experiment results showed that the cross validation and prediction accuracy in the model based on characteristic wavelengths were 95% and 96%, the cross validation and prediction accuracy in the model based on optimal PCs were 94% and 98%. It indicated that the two kinds of simplified models all achieved the promising results and they all had the comparable discriminant power for rice adulteration when compared with the full-spectral-SVM. The results demonstrated that it is feasible to use hyperspectral imaging technology for the detection of the problem of rice adulteration.
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Article ID:2807Article Name:ENVI294Last Updated:8/6/:03 AMProducts:ENVITopic:&As an aid in interpreting the results of a Principal Components Analysis, it can be quite helpful to determine the relative contributions of each input band to the new PCA bands. This Tech Tip explains how to find this information in ENVI and provides a convenient user function for sorting the PCA results by original input band contribution.Discussion:Some Background&ENVI's Principal Components Analysis, or PCA, is a linear transformation which reorganizes the variance in a multiband image into a new set of image bands (see&). Each individual band in the output PCA image receives some contribution from&all&of the input image's bands. The amount that each original input band contributes to each PCA band can be determined by examining the eigenvectors from the PC analysis, which are stored in the PCA statistics file. The eigenvectors define the orientation of the principal component axes in n-dimensional space, or more simply, the direction that the axis points. There is one eigenvector for each PCA Band, and each eigenvector contains one element for each input band. For example, a PC rotation of a 128-band input image would produce 128 PCA Bands and 128 corresponding eigenvectors that each have 128 elements (where the first element of the eigenvector corresponds to input band 1, the second element to input band 2, etc.). The value of an eigenvector element is proportional to the input band's contribution to the PCA Band. Because of the mathematics involved in computing the eigenvectors, the weighting of each input band is computed by squaring the input band's eigenvector element. Thus, the total contribution of all of the input bands to any given PCA Band is the sum of the squares of the PCA Band's eigenvector elements. To determine the contribution that any individual band made, simply square that band's element in the eigenvector and divide it by the sum of squares of the entire eigenvector:Contribution of band&b&= eigenvector(b)2&/&SUM(for&i=1 to n) [ eigenvector(i)2&]where:b&= the input band number, andn = the total number of input bandsComputing the PCA Band Contributions in ENVIComputing the PCA band weightings in ENVI is made simple by the fact that ENVI normalizes its eigenvectors to be&unit length, meaning that the sum of squares for every eigenvector is exactly one (i.e., the denominator in the equation above is always 1.0). Thus, the original band contributions to a PCA band can be computed by simply squaring the eigenvector matrix from the PCA Stats file. To compute the band weightings in ENVI, follow these steps:Using&Basic Tools -& Statistics -& View Statistics File&select the PCA statistics file. In the&View Statistics File&dialog, check the Covariance Statistics and Covariance Image boxes.Click&OK&to read the eigenvector matrix into ENVI as an image. The eigenvector matrix in the stats file contains the individual eigenvectors in its columns, however the eigenvector image contains the eigenvectors in its&rows.Using&Basic Tools -& Band Math&enter the expression:(b1^2)*100Map the&b1&variable to the eigenvector image.When the Band Math result appears in the&Available Bands List, display it into an image window. From the image display's&Functions&menu choose&Profiles -& X Profile.Clicking and dragging your cursor in the X Profile window will display the data values at the bottom left margin of the plot. The x-axis values in the plot represent the original input image band numbers and the y-axis values represent the percent contribution to the PCA band. The PCA Band number is equal to the row number of the image from which the X Profile was extracted. For example, the figure below the shows the Zoom window display for a 6 band eigenvalue image that has been adjusted using the Band Math expression in step 3. An X Profile has been extracted for the second row of this image, corresponding to the 2nd PC Band. A cursor query in the plot window shows that Band 3 of the original image contributed 7.59% to PC 2.User function for sorting a PCA by band contributionThe user function included here provides a convenient way to display the original band contributions to a PCA in a spreadsheet table. The data in the table can be sorted by highlighting any row (PC) or column (band).&
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Copyright &利用ENVI对高光谱图像数据得到主成分分析图像后，如何知道各主成分图像的权重 系数？_百度知道
利用ENVI对高光谱图像数据得到主成分分析图像后，如何知道各主成分图像的权重 系数？
这里先谢谢大家了
我有更好的答案
权重系数表自动弹出的啊~~
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