基于高光谱成像技术的红酸枝木材种类识别

Identifying Dalbergia spp. wood with hyperspectral imaging technology

为了实现市场上常见红酸枝类Dalbergia spp.木材的快速无损识别,利用高光谱成像技术对不同红酸枝木材进行种类识别研究。以交趾黄檀Dalbergia cochinchinensis,巴里黄檀Dalbergia bariensis,奥氏黄檀Dalbergia oliveri和微凹黄檀Dalbergia retusa为研究对象,采集高光谱图像并提取感兴趣区域内的反射光谱,采用Savitsky-Golay（SG）平滑算法、标准正态变量变换（SNV）和多元散射校正（MSC）对955~1 642 nm波段光谱进行预处理,并通过主成分分析法（PCA）,回归系数法（RC）以及连续投影法（SPA）选择特征波长,分别建立了偏最小二乘判别分析（PLSDA）和极限学习机（ELM）判别分析模型。研究结果表明：经SG和MSC光谱预处理,采用SPA选择的特征波长建立的ELM模型性能最优,建模集和预测集的识别率均为100.0%。这为红酸枝木材种类的快速无损识别提供了新的方法。

To rapidly and nondestructively identify common Ddbergia spp. of wood on the market, wood from Dalbergia spp. （D. cochiuchiueusis, D. barieusis, D. oliveri, and D. retusa） was identified using hyperspectral imaging technology. The hyperspectral images were collected and the reflectance spectral from the region of interest were extracted from the images. Wavelengths from 955 to 1 642 nm were preprocessed by Savitsky-Golay smoothing （SG）, standard normal variate （SNV）, and Multiplicative Scatter Correction （MSC）. Then, a partial least square-discriminant analysis （PLS-DA） and an extreme learning machine （ELM） were used to build dis- criminant models based on selected sensitive wavelengths using principal component analysis （PCA）, regression coefficient （RC）, and successive projections algorithm （SPA） from the preprocessed spectra. Results showed that for selected sensitive wavelengths using SPA from SG and MSC preprocessed spectra, ELM models obtained the best classification accuracy （100.0%） for both the calibration set and the prediction set. Thus, this study provided a new method to identify Dalbergia spp. wood rapidly and nondestructively. [ Ch, 5 fig. 4 tab. 17 ref. ]