文献类型：期刊文献

英文题名：Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

作者：Pan, Xi[1] Li, Kang[1,2] Chen, Zhangjing[3] Yang, Zhong[1]

通信作者：Yang, Z[1]

机构：[1]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China;[2]Hubei Technol Exchange, Wuhan 430071, Peoples R China;[3]Virginia Polytech Inst & State Univ, Dept Sustainable Biomat, Blacksburg, VA 24060 USA

年份：2021

卷号：12

期号：11

外文期刊名：FORESTS

收录：;EI(收录号：20214611155510);Scopus(收录号：2-s2.0-85118859893);WOS:【SCI-EXPANDED(收录号:WOS:000725335300001)】；

基金：FundingThis research was funded by the National Natural Science Foundation of China, Grant Numbers 31770766 and 31370711.

语种：英文

外文关键词：near-infrared (NIR) spectra; gray-level co-occurrence matrix (GLCM); wood identification; feature fusion; support vector machine (SVM)

摘要：Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780-2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780-1100 nm and 1100-2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.