Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features
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)...
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MDPI AG
2021
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oai:doaj.org-article:8e35ca9b5e8746fbad7f53359b86a2642021-11-25T17:38:17ZIdentifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features10.3390/f121115271999-4907https://doaj.org/article/8e35ca9b5e8746fbad7f53359b86a2642021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1527https://doaj.org/toc/1999-4907Identifying 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.Xi PanKang LiZhangjing ChenZhong YangMDPI AGarticlenear-infrared (NIR) spectragray-level co-occurrence matrix (GLCM)wood identificationfeature fusionsupport vector machine (SVM)Plant ecologyQK900-989ENForests, Vol 12, Iss 1527, p 1527 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
near-infrared (NIR) spectra gray-level co-occurrence matrix (GLCM) wood identification feature fusion support vector machine (SVM) Plant ecology QK900-989 |
| spellingShingle |
near-infrared (NIR) spectra gray-level co-occurrence matrix (GLCM) wood identification feature fusion support vector machine (SVM) Plant ecology QK900-989 Xi Pan Kang Li Zhangjing Chen Zhong Yang Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| description |
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. |
| format |
article |
| author |
Xi Pan Kang Li Zhangjing Chen Zhong Yang |
| author_facet |
Xi Pan Kang Li Zhangjing Chen Zhong Yang |
| author_sort |
Xi Pan |
| title |
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| title_short |
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| title_full |
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| title_fullStr |
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| title_full_unstemmed |
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features |
| title_sort |
identifying wood based on near-infrared spectra and four gray-level co-occurrence matrix texture features |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/8e35ca9b5e8746fbad7f53359b86a264 |
| work_keys_str_mv |
AT xipan identifyingwoodbasedonnearinfraredspectraandfourgraylevelcooccurrencematrixtexturefeatures AT kangli identifyingwoodbasedonnearinfraredspectraandfourgraylevelcooccurrencematrixtexturefeatures AT zhangjingchen identifyingwoodbasedonnearinfraredspectraandfourgraylevelcooccurrencematrixtexturefeatures AT zhongyang identifyingwoodbasedonnearinfraredspectraandfourgraylevelcooccurrencematrixtexturefeatures |
| _version_ |
1718412177911578624 |