Information fusion technology for terahertz spectra and hyperspectral imaging in wood species identification
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Materials Science,Forestry
Link
https://link.springer.com/content/pdf/10.1007/s00107-023-02027-1.pdf
Reference24 articles.
1. Awais M, Altgen M, Mäkelä M, Belt T, Rautkari L (2022) Quantitative prediction of moisture content distribution in acetylated wood using near-infrared hyperspectral imaging. J Mater Sci. https://doi.org/10.1007/s10853-021-06812-2
2. Chambi-Legoas R, Tomazello-Filho M, Vidal C, Chaix G (2023) Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees. Trees 37(3):981–991. https://doi.org/10.1007/s00468-023-02397-2
3. Chen J, Li G (2020) Prediction of moisture content of wood using modified random frog and vis-NIR hyperspectral imaging. Infrared Phys Technol 105:103225. https://doi.org/10.1016/j.infrared.2020.103225
4. Dorney TD, Baraniuk RG, Mittleman DM (2001) Material parameter estimation with terahertz time-domain spectroscopy. JOSA A 18(7):1562–1571
5. Duan R, Wang Y, Zhao L, Da Yun X, Zhou N (2022) Prediction of wood moisture content based on THz time-domain spectroscopy. BioResources. https://doi.org/10.15376/biores.17.3.4745-4762
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Unsupervised Wood Species Identification Based on Multiobjective Optimal Clustering and Feature Fusion;2024-08-06
2. Impact of surface quality on the identification of tropical wood species using benchtop and portable NIR instruments;2024-07-29
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