高光谱成像的油菜和杂草分类方法

发布时间：2018-04-15 10:13

本文选题：高光谱 + 油菜　；参考：《光谱学与光谱分析》2017年11期

【摘要】：利用高光谱成像技术结合化学计量学方法对油菜中的杂草进行分类识别。采用近红外高光谱技术,通过正态变量变换(SNV)、去趋势化(De-trending)、多元散射校正(MSC)、移动平均平滑法(MA)、多项式卷积平滑法(SG)、基线校正(baseline)及归一化(normalize)算法对光谱数据进行预处理,采用主成分载荷(PCA loadings)、载荷系数法(x-LW)、回归系数法(RC)、连续投影算法(SPA)分别进行特征波长提取,采用偏最小二乘判别分析(PLS-DA)、极限学习机(ELM)和支持向量机(SVM)建立分类模型。结果表明,基于De-trending预处理,通过PCA loadings,x-loading weights及SPA特征波长提取方法,基于极限学习机ELM算法建立的模型取得了最优的分类效果,建模集和预测集的分类精度均达到100%,另引入平均分类精度的指标,发现不同试验时间下,模型分类精度变化不大,表明应用近红外高光谱成像技术对油菜和杂草进行分类是可行的。
[Abstract]:The weeds in rapeseed were classified and identified by hyperspectral imaging and chemometrics.Using near infrared hyperspectral technique, spectral data are preprocessed by normal variable transform (SNV), De-trending, multivariate scattering correction, moving average smoothing, polynomial convolution smoothing, baseline correction and normalized normalization.Principal component loading (PCA), load coefficient method (LW), regression coefficient method (RCN) and continuous projection algorithm (SPA) were used to extract the feature wavelength, and the classification models were established by partial least squares discriminant analysis (PLS-DAA), extreme learning machine (ELM) and support vector machine (SVM).The results show that based on the De-trending pretreatment, the model based on the ELM algorithm of extreme learning machine and the method of PCA loading x loading weights and SPA feature wavelength extraction have obtained the best classification effect.The classification accuracy of both the modeling set and the prediction set is 100. By introducing the index of average classification accuracy, it is found that the model classification accuracy does not change much under different test time.The results show that it is feasible to classify rape and weeds by near infrared hyperspectral imaging.
【作者单位】：浙江大学生物系统工程与食品科学学院;
【基金】：国家重点研发计划(2016YFD0300606) 浙江省科技计划项目(2015C02008)资助
【分类号】：S451.0;S565.4;TP79

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