|
|
|
|
|
|
| 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
|
|
|
|
|
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.
|
|
Received: 2021-06-17
Accepted: 2021-11-23
|
|
|
|
Corresponding Authors:
SHI Jun-sheng
E-mail: shi-js@263.net
|
|
[1] Hajipour A, Shams-Nateri A. Color Research and Application, 2017, 42(2): 182.
[2] WU Zi-yong, JIANG Jin-bao, GUO Jian-wei，et al(吴自勇, 蒋金豹, 郭监威, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(10): 3123.
[3] Shibata T, Tanaka M，Okutomi M. Proc SPIE, Digital Photography XI, 2015, 9404: 94040G.
[4] Jee S, Kang M. Sensors, 2019, 19(5): 1256.
[5] Soria X, Sappa A，Hammoud R. Sensors, 2018, 18(7): 2059.
[6] TONG Qing-xi, ZHANG Bing, ZHANG Li-fu(童庆禧, 张 兵, 张立福). Journal of Remote Sensing(遥感学报), 2016, 20(5): 689.
[7] PENG Ni-na, ZHOU Jian-min, LUO Jun，et al(彭妮娜, 周建民, 罗 军，等). Journal of Atmospheric and Environmental Optics(大气与环境光学学报), 2006, 1(2): 92.
[8] Kohonen O, Parkkinen J, Jaaskelainen T. Color Research and Application, 2006, 31(5): 381.
[9] Chen Y S, Lin Z H, Zhao X. et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094.
[10] Xiao K D, Zhu Y T, Li C J，et al. Optics Express, 2016, 24(13): 14934.
[11] Liu L Y, Song B W, Zhang S，et al. Remote Sensing, 2017, 9(11): 1113.
[12] Otsu H, Yamamoto M，Hachisuka T. Computer Graphics Forum, 2018, 37(6): 370.
[13] Dadon A, Mandelmilch M, Ben-Dor E，et al. Remote Sensing, 2019, 11(23): 2800.
|
| [1] |
JIANG Dan-yang1, WANG Zhi-feng1*, GAO Cheng1, 2, LI Chang-jun1. Spectral Reflectance Reconstruction With Color Constancy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1044-1048. |
| [2] |
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076. |
| [3] |
LI Fu-hao, LI Chang-jun*. Spectral Reflectance Reconstruction Based on Camera Raw RGB Using Weighted Third-Order Polynomial and Wiener Estimation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3281-3285. |
| [4] |
ZHANG Shi-ya1, LÜ Xiao-min2, 3*, ZHOU Guang-sheng2, 3, 4*, REN Hong-rui1. Spectral Characteristics During Leaf Flourishing Development of Quercus Mongolica and Its Influencing Factors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2924-2929. |
| [5] |
ZHENG Yu-dong1, XU Yun-cheng1, YAN Hai-jun1*, ZHENG Yong-jun2. Analysis of Influencing Factors in Wheat/Maize Canopy RVI and NDVI Acquisition Using Ground-Based Remote Sensing System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2578-2585. |
| [6] |
ZHAO Shou-bo1,2, LI Xiu-hong1,2. Research on Reflection Spectrum Reconstruction Algorithm Based on Compressed Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1092-1096. |
| [7] |
QU Dong-sheng, HONG Yan-ji, ZHU Xiao-hui. Measurements of H2O Spectroscopic Parameters Based on the Tunable Diode Laser Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1072-1078. |
| [8] |
CHANG Tao, XIE Xin, GUAN Mei, ZHANG Qiu-ping, ZHANG Zhen-qian*, GUAN Chun-yun. Analysis of the Relationship Between Hyperspectral Reflectance and Yield of Rape Under Different Micro Fertilizer Conditions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 552-557. |
| [9] |
NING Jia-lian1, TANG Jin1, HU Tian-you1, LIU Qiang2, WANG Hao-wen1, CHEN Zhi-li1*. Study on Spectral Radiation Characteristics of Carbon Disulfide Flame Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1377-1381. |
| [10] |
LU Wan-hong, LI Peng*, WANG Chu-biao, LIN Yan, LUO Jian-zhong. Identifying Eucalypt Hybrids and Cross Parents by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 873-877. |
| [11] |
WANG Jin-feng1, 2, 5, WANG Shi-jie2, 3, BAI Xiao-yong2, 3*, LIU Fang1, LU Qian1, 2, TIAN Shi-qi2, 4, WANG Ming-ming2. Prediction Soil Heavy Metal Zinc Based on Spectral Reflectance in Karst Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3873-3879. |
| [12] |
CHEN Chun-xia1, 2, XIU Lian-cun1, 2*, GAO Yang1, 2. Research on Data Processing of Core Spectral Scanner[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1630-1636. |
| [13] |
KONG Ling-jun1, ZENG Xi2*, ZHANG Lei-hong2, ZHAN Wen-jie2, ZENG Wen-chao3. Research on Spectral Reflectance Reconstruction Based on Genetic Algorithm for Selecting Multi-Illuminants[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1162-1168. |
| [14] |
ZHANG Su-lan1, 2, 3, QIN Ju1, TANG Xiao-dong1, WANG Yu-jie1, HUANG Jin-long1*, SONG Qing-liang2, MIN Jia-yuan2. Spectral Characteristics and Evaluation Model of Pinus Massoniana Suffering from Bursaphelenchus Xylophilus Disease[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(03): 865-872. |
| [15] |
LU De-jun, CUAN Kai-xuan, ZHANG Wei-feng*. Research on Spectral Reflectance Estimation Using Locally Weighted Linear Regression within k-Nearest Neighbors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3708-3712. |
|
|
|
|
