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9 November 2018 Spectral reflectance measurement and the principal component analysis and correlation analysis of trees in visible and near infrared
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Abstract
Visible and near-infrared spectral reflectances of surface vegetation are basic data for applications in remote sensing classification, multispectral imaging and color reproduction. Leaves are the objects of this study. Firstly, The 400-700 nm visible light spectral reflectance and 700−1000 nm near infrared spectral reflectance data of 12 kinds of trees such as camphor tree, ginkgo tree and peach tree (etc.) are measured by visible and near-infrared portable hyperspectral cameras. The spectral reflectance data is obtained by denoising the using the Minimum Noise Fraction (MNF). Secondly, the Principal Component Analysis (PCA) is used as a method of processing spectral reflectance in the visible and near infrared bands. At last, the correlation analysis is used for spectral reflectance in the visible and near-infrared bands. The obtained data and results provide a theoretical basis for the subsequent establishment of a spectral reflectance data base of surface vegetation spectroscopy and multispectral imaging.
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Zhenhua Du, Junsheng Shi, Feiyan Cheng, Xiaoqiao Huang, and Lin Xu "Spectral reflectance measurement and the principal component analysis and correlation analysis of trees in visible and near infrared", Proc. SPIE 10826, Infrared, Millimeter-Wave, and Terahertz Technologies V, 1082617 (9 November 2018); https://doi.org/10.1117/12.2501165
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