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| Establishment of Visible and NIR Spectral Reflectance Database of Plant Leaves and Principal Component Analysis |
| JIANG Wan-li1, 2, SHI Jun-sheng1, 2*, JI Ming-jiang1, 2 |
1. School of Physics and Electronic Information, Yunnan Normal University, Kunming 650504, China
2. Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming 650504, China
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Abstract Visible and near-infrared spectral reflectance is the basic database for research and application in color science and technology and remote sensing object classification and recognition.The principal component analysis (PCA) is widely used in spectral data analysis, spectral reconstruction, hyperspectral data dimension reduction, and remote sensing image classification. In this paper, a database of spectral reflectance from visible light to near-infrared of 150 leaves of 48 plants, including Salix, Cinnamomum camphora (L.) Presl, Dracaena marginata, and Jacaranda mimosifolia, etc. Which are common in park greenery of Yunnan, isestablished. The wavelength range from 400 to 1 000 nm with 4 nm intervals. The PCA wascarried out on the visible and from visible to near-infrared wavebands respectively.The measurement results show that the spectral reflectance of different vegetation leaves according to the same hue of red, green and yellow are the same, For the same plant,in the visible waveband, the spectral reflectances are quite different because of the different content of chlorophyll, lutein, carotene and anthocyanin in the body.The spectral reflectance of all plant leaves in the near-infrared waveband is only different in amplitude, while the spectral reflectance of the same plant does not change with wavelength.The PCA shows that the cumulative contribution rates of the first three principal components in the visible and visible near-infrared wavebands reached 98.62% and 94.97% respectively.The database and results of PCA provide support for the spectral reconstruction of natural objects, the multispectral imaging technology and the classification and recognition of the target of remote sensing images.
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Received: 2021-06-17
Accepted: 2021-11-23
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Corresponding Authors:
SHI Jun-sheng
E-mail: shi-js@263.net
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