基于高光谱成像技术融合光谱和图像特征鉴别不同产地的甘草

Fusion of spectrum and image features to identify Glycyrrhizae Radix et Rhizoma from different origins based on hyperspectral imaging technology

基于高光谱成像技术从可见-近红外波段(VNIR, 435~1 042 nm)和短波红外(SWIR, 898~1 751 nm)波段提取光谱和图像特征,融合数据建立分类模型,鉴别不同产地甘草药材。提取甘草样品高光谱数据的光谱特征,结合多种预处理算法对光谱数据进行降噪处理,利用偏最小二乘判别分析(PLS-DA)、支持向量机分类(SVC)和随机森林(RF)建立产地分类模型。利用灰度共生矩阵(GLCM)提取图像纹理特征,从3个维度实现全方位融合,即VNIR和SWIR融合,光谱和图像融合以及全数据融合。结果显示,短波红外波段光谱对甘草产地鉴别准确率较高,基于Savitzky-Golay平滑的二阶导数预处理光谱建立的产地鉴别模型具有较优判别效果。PLS-DA和SVC产地鉴别模型分类准确率分别达到93.40%和94.11%,基于混淆矩阵和ROC特征曲线对模型进行评估,PLS-DA模型分类准确度和模型泛化能力优于SVC和RF模型。3个维度上的数据融合对分类性能产生积极影响,连续投影算法波段全数据融合仅利用28个特征波长分类准确率可达到94.82%,该方法在保证分类准确率的基础上能够有效地提高分类效率。高光谱成像技术能够无损、直观、快速鉴别不同产地甘草样品。

To identify Glycyrrhizae Radix et Rhizoma from different geographical origins, spectrum and image features were extracted from visible and near-infrared(VNIR, 435-1 042 nm) and short-wave infrared(SWIR, 898-1 751 nm) ranges based on hyperspectral imaging technology. The spectral features of Glycyrrhizae Radix et Rhizoma samples were extracted from hyperspectral data and denoised by a variety of pre-processing methods. The classification models were established by using Partial Least Squares Discriminate Analysis(PLS-DA), Support Vector Classification(SVC) and Random Forest(RF). Meanwhile, Gray-Level Co-occurrence matrix(GLCM) was employed to extract textural variables. The spectrum and image data were implemented from three dimensions, including VNIR and SWIR fusion, spectrum and image fusion, and comprehensive data fusion. The results indicated that the spectrum in SWIR range performed better classification accuracy than VNIR range. Compared with other four pre-processing methods, the second derivative method based on Savitzky-Golay(SG) smoothing exhibited the best performance, and the classification accuracy of PLS-DA and SVC models were 93.40% and 94.11%, separately. In addition, the PLS-DA model was superior to SVC and RF models in terms of classification accuracy and model generalization capability, which were evaluated by confusion matrix and receiver operating characteristic curve(ROC). Comprehensive data fusion on SPA bands achieved a classification accuracy of 94.82% with only 28 bands. As a result, this approach not only greatly improved the classification efficiency but also maintained its accuracy. The hyperspectral imaging system, a non-invasively, intuitively and quickly identify technology, could effectively distinguish Glycyrrhizae Radix et Rhizoma samples from different origins.