1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
2. Liaoning Agricultural Information Engineering Technology Research Center, Shenyang 110866, China
Abstract：Conducting the unmanned aerial vehicle (UAV) hyperspectral unmixing of rice and obtaining the hyperspectral reflectance information of rice plants is of great significance for improving the accuracy of the inversion model of rice physical and chemical parameters. Most of the current research is based on the data of hyperspectral remote sensing images themselves for demixing. That is, unmixing of hyperspectral data is carried out by using algorithm model. In this study, the advantages of hyperspectral images and visible spectral images were complemented, and a hyperspectral unmixing method for UAV in rice field was based on the fusion of UAV high-definition images and hyperspectral images remote sensing images was proposed. This method solved the problem of the limitation of single data and enhanced the description ability of spectral data for ground objects. In order to better calculate the endmember abundance, the high-definition digital orthophotos of the target area were spatially aligned with the UAV hyperspectral remote sensing images, so that the pictures obtained by different sensors were aligned in geometric positions. The supervised classification method of the SVM classifier was used to classify the digital orthophoto of visible light, and the result of the classification was used to correspond to a pixel of the hyperspectrum to obtain the endmember abundance within a pixel. Suppose the endmembers of the water body in adjacent areas were the same, the linear unmixing model (LSMM) was used to unmix the mixed pixels in the adjacent area and finally the hyperspectral reflectance information of rice was obtained. The results showed that the spatial registration of the two images enriches the data source information, which was beneficial to the endmember abundance calculation of the pixels. Among them, the unmixing effect of rice endmember abundance above 70% was the best, the unmixing effect of abundance above 50% was general, and the unmixing effect was poor when the abundance was below 30%. Use the supervised classification method to classify the ground objects with an accuracy of 99.5%, and the classification accuracy of the object-oriented method was 98.2%, the supervised classification method was better than the object-oriented classification method. The final decomposition reflectance of the mixed pixel was higher than that of the original mixed pixel, which reduced the influence of the mixed part of the water body on the spectral data, and made the spectral reflectance of the rice after decomposition more accurate. This research could provide a theoretical basis for the inversion of the UAV imaging hyperspectral remote sensing of rice physical and chemical parameters.
[1] CHANG Jun-yan, SONG Ming-yang, YU Xiao-man, et al(常俊彦，宋明阳，于晓曼，等). Journal of Agro-Environment Science(农业环境科学学报), 2018, 37(8): 1793.
[2] HE Yong, PENG Ji-yu, LIU Fei, et al(何 勇，彭继宇，刘 飞，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(3): 174.
[3] SUN Xiao-xiang, WANG Fang-dong, GUI Xi, et al(孙小香，王芳东，郭 熙，等). Acta Agriculturae Universitatis Jiangxiensis(江西农业大学学报), 2018, 40(3): 444.
[4] LI Shu-tao, LI Cong-yu, KANG Xu-dong(李树涛, 李聪妤, 康旭东). Journal of Remote Sensing(遥感学报), 2021, 25(1): 148.
[5] Rischbeck P，Cardellach P，Mistele B，et al. Journal of Agronomy & Crop Science, 2017, 203(6): 438.
[6] XIE Xing-yu, HE Hui, XING Hai-hua(谢幸雨，贺 辉，邢海花). Journal of Data Acquisition and Processing(数据采集与处理), 2021, 36(1): 156.
[7] Abedinzadeh N，Shariat M，Monavari S M，et al. Process Safety and Environmental Protection，2018，18(1): 82.
[8] FENG Shuai, XU Tong-yu, YU Feng-hua, et al(冯 帅，许童羽，于丰华，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(10): 3281.
[9] YU Feng-hua, CAO Ying-li, XU Tong-yu, et al(于丰华，曹英丽，许童羽，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(15): 103.
[10] YANG Guang, TIAN Zhang-nan, LI Hao, et al(杨 桄，田张男，李 豪，等). Laser Technology(激光技术), 2020, 44(2): 143.
[11] MENG Xiang-chao, SUN Wei-wei, REN Kai, et al(孟祥超，孙伟伟，任 凯，等). Journal of Remote Sensing(遥感学报), 2020, 24(4): 379.
