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A Comparative Study on Roujean and Ross Li Models of Winter Jujube in South Xinjiang Under Different Outdoor Light |
SUO Yu-ting1,2, LUO Hua-ping1,2*, LIU Jin-xiu1,2, LI Wei1,2, CHEN Chong3, XU Jia-yi1,2, WANG Chang-xu1,2 |
1. College of Mechanic and Electrical, Tarim University, Alar 843300, China
2. Xinjiang Uyger Autonomous Region General Institutes of Higher Education Key Lab of Modern Agriculture Engineering, Alar 843300, China
3. Baicheng Normal University, Baicheng 137000, China |
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Abstract How to eliminate or reduce the difference of inversion data and improve the detection accuracy under different illumination is a big problem in the outdoor detection of winter jujube in South Xinjiang. Therefore, this paper obtains the bidirectional reflection distribution function of winter jujube in South Xinjiang by using a hyperspectral camera The least square method is used to fit the parameters of the roujean model and Ross Li model. Finally, the inversion results of the roujean model and Ross Li model are compared, and the viewpoint of which weather, which wavelength and which model are the best is put forward. The experimental results show that: (1) in cloudy weather, in an inversion of Dolp of winter jujube in South Xinjiang,The R-square of Ross Li model is 0.974 8 and that of roujean model is 0.969 9; the R-square of Ross Li model is 0.972 3 and that of roujean model is 0.974 9 when the intensity component of winter jujube is retrieved. In cloudy weather, in an inversion of Dolp of winter jujube in South Xinjiang,the R-square of Ross Li model is 0.965 1, and that of roujean model is 0.977 8; in an inversion of winter jujube intensity component, the R-square of Ross Li model is 0.942 0, and that of roujean model is 0.968 8. In sunny weather, in an inversion of Dolp of winter jujube in South Xinjiang,R-square of Ross Li model is 0.965 5, R-square of roujean model is 0.926 2; in an inversion of winter jujube intensity component, R-square of Ross Li model is 0.928 5, R-square of roujean model is 0.833 1. The best scheme of the whole inversion is to use the Ross Li model for the inversion of winter jujube DOLP in cloudy weather, roujean model for the inversion of intensity component, Ross Li model for the inversion of winter jujube DOLP and intensity component in sunny weather, and roujean model for the inversion of winter jujube DOLP and intensity component in cloudy weather. (2) The best scheme of multi band inversion is: in cloudy weather, when the intensity component of winter jujube in South Xinjiang is retrieved, the wavelength is 1 000~1 100 nm, Ross Li model is needed, the wavelength is 1 450~1 600 nm, roujean model is needed, and the other two models can be used; when the DOLP of winter jujube in South Xinjiang is retrieved, the wavelength is near 1 300 nm, Ross Li model is needed, and the other two models can be used. In cloudy weather, when the intensity component of winter jujube in South Xinjiang is retrieved, the wavelength is 1 000~1 350 nm, roujean model is needed, the wavelength is near 1 600 nm, Ross Li model is needed; when the Dolp of winter jujube in South Xinjiang is retrieved, in the range of 1 000~1 350 nm, roujean model is needed, and in the range of 1 600 nm, Ross Li model is needed. On sunny days, when retrieving the intensity component of winter jujube in South Xinjiang, the wave-length is in the range of 1 000~1 350 nm and around 1 600 nm, the Ross Li model is needed, and there is no special requirement for other wave-bands; when retrieving DOLP of winter jujube in South Xinjiang, the wavelength is near 1 000 nm, the roujean model is needed, and the wavelength is near 1 600 nm the Ross Li model is needed. Thus, the method of eliminating or reducing the difference of inversion data is explored, which lays a foundation for improving the accuracy of outdoor detection of winter jujube in South Xinjiang.
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Received: 2020-05-30
Accepted: 2020-09-10
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Corresponding Authors:
LUO Hua-ping
E-mail: luohuaping739@163.com
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[1] Zhu Gaolong, Ju Weimin, Chen Jingming, et al. Journal of Remote Sensing, 2011, 15(5): 875.
[2] Dinh Ngo Thi, Nguyen Thi Thu Ha, Quy Tran Dang,et al. Remote Sensing, 2019, 11:716.
[3] De Castro A I, Ehsani R, Ploetz R, et al. Remote Sensing of Environment, 2015, 171: 33.
[4] Calderón R, Navas-Cortés J A, Lucena C, et al. Remote Sensing of Environment, 2013, 139: 231.
[5] Rebecca L Whetton, Toby W Waine, Abdul M Mouazen. Biosystems Engineering, 2018, 167: 144.
[6] Bagheri Nikrooz. Computers and Electronics in Agriculture, 2020, 168(C): 1.
[7] Hyde M W, Schmidt J D, Havrilla M J. Optics Express, 2009, 17(24): 22138.
[8] Wang Kai, Zhu Jingping, Liu Hong, et al. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 2017, 34(2): 259.
[9] Yang Min, Xu Wenbin, Sun Zhenyuan, et al. Optics Communications, 2019, 453: 1.
[10] ZHU Gao-long, JU Wei-min, CHEN Jing-ming, et al(朱高龙, 居为民, 陈镜明,等). Journal of Remote Sensing(遥感学报), 2011, 15(5): 875.
[11] CHANG Ya-xuan, JIAO Zi-di, DONG Ya-dong, et al(常雅轩, 焦子锑, 董亚冬,等). Journal of Remote Sensing(遥感学报), 2019, 23(4): 661.
[12] Roujean J L. Journal of Remote Sensing(遥感学报), 1997,(S1): 170.
[13] Lovell J L, Graetz R D. International Journal of Remote Sensing, 2002, 23(14): 2767.
[14] Grant I F. Remote Sensing Reviews, 2000, 19(1-4): 243.
[15] Lunagaria. International Journal of Remote Sensing, 2020, 41(9): 3627. |
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