| 基于高光谱成像技术的红酸枝木材种类识别 |
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| 引用本文： | 倪茜茜,祁亨年,周竹,汪杭军.基于高光谱成像技术的红酸枝木材种类识别[J].浙江农林大学学报,2016,33(3):489-494. |
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| 作者姓名： | 倪茜茜 祁亨年 周竹 汪杭军 |
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| 作者单位： | 1.浙江农林大学 信息工程学院，浙江 临安 3113002.浙江农林大学 浙江省林业智能监测与信息技术研究重点实验室，浙江 临安 3113003.浙江农林大学 暨阳学院，浙江 诸暨 311800 |
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| 基金项目： | 浙江省自然科学基金资助项目LQ13F050006, Y14C130046浙江农林大学科研发展基金资助项目2013FR059;2012FR085 |
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| 摘 要： | 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（PLS-DA）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。图5表4参17
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| 关 键 词： | 木材科学与技术 高光谱成像 特征波长 红酸枝木材 无损判别 |
| 收稿时间： | 2015-05-27 |
Identifying Dalbergia spp. wood with hyperspectral imaging technology |
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| NI Qianqian,QI Hengnian,ZHOU Zhu,WANG Hangjun.Identifying Dalbergia spp. wood with hyperspectral imaging technology[J].Journal of Zhejiang A&F University,2016,33(3):489-494. |
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| Authors: | NI Qianqian QI Hengnian ZHOU Zhu WANG Hangjun |
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| Affiliation: | 1.School of Information Engineering, Zhejiang A & F University, Lin’an 311300, Zhejiang, China2.Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Zhejiang A & F University, Lin’an 311300, Zhejiang, China3.Jiyang College, Zhejiang A & F University, Zhuji 311800, Zhejiang, China |
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| Abstract: | To rapidly and nondestructively identify common Dalbergia spp. of wood on the market, wood from Dalbergia spp. (D. cochinchinensis, D. bariensis, 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 discriminant 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.] |
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