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- W2899875601 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." @default.
- W2899875601 created "2018-11-16" @default.
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- W2899875601 date "2018-11-09" @default.
- W2899875601 modified "2023-09-27" @default.
- W2899875601 title "Spectral reflectance measurement and the principal component analysis and correlation analysis of trees in visible and near infrared" @default.
- W2899875601 cites W1545447931 @default.
- W2899875601 doi "https://doi.org/10.1117/12.2501165" @default.
- W2899875601 hasPublicationYear "2018" @default.
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