苏伟,谢茈萱,王伟,金添,王新盛.玉米冠层LAI反演中UAV影像镜面反射去除方法[J].农业机械学报,2020,51(5):173-181.
SU Wei,XIE Zixuan,WANG Wei,JIN Tian,WANG Xinsheng.Specular Reflection Removal of UAV Image in Corn Canopy LAI Inversion[J].Transactions of the Chinese Society for Agricultural Machinery,2020,51(5):173-181.
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玉米冠层LAI反演中UAV影像镜面反射去除方法   [下载全文]
Specular Reflection Removal of UAV Image in Corn Canopy LAI Inversion   [Download Pdf][in English]
投稿时间:2019-09-10  
DOI:10.6041/j.issn.1000-1298.2020.05.019
中文关键词:  无人机影像  叶面积指数  反演  镜面反射去除  小波变换
基金项目:国家重点研发计划项目(2017YFD0300903)、国家自然科学基金项目(41671433)和中央高校基本科研业务费专项资金项目(2019TC117、2019TC138)
作者单位
苏伟 中国农业大学 
谢茈萱 中国农业大学 
王伟 中国农业大学 
金添 中国农业大学 
王新盛 中国农业大学 
中文摘要:针对玉米叶片反射太阳光时因镜面反射导致获得的无人机影像反射率中存在与冠层结构无关的镜面反射部分,从而影响玉米冠层LAI的反演精度问题,本研究利用小波变换对无人机影像不同波段的阈值设置,在不影响漫反射的前提下削弱镜面反射成分,尽量只保留与冠层结构有关的反射率成分。以2018年7月15日和7月26日获取的河北农业大学辛集试验站多光谱无人机影像为数据源,构建了NDVI、GNDVI、SAVI和EVI 4个植被指数,并分别与ln(LAI)构建玉米冠层的单变量反演模型,利用决定系数和均方根误差进行LAI反演精度评价。精度评价结果表明,在7月15日玉米植株较稀疏时,去除镜面反射后,4个植被指数反演LAI与实测LAI的决定系数分别从0.7190、0.5598、0.6241、0.5985上升至0.7633、0.6940、0.6497、0.6194,均方根误差分别从0.2244、0.2526、0.2214、0.2245下降到0.1880、0.1958、0.1918、0.1987,说明去除镜面反射可以提高LAI的反演精度。在7月26日玉米植株相对茂密时,去除镜面反射后,4个指数构建模型对应的决定系数也同样提高,但在这种情况下,NDVI和GNDVI容易发生饱和,用阈值法降低反射率反而会加剧饱和现象,使这2个指数不能充分反映LAI的变化。SAVI和EVI因为加入了冠层背景调整因子,植被指数的变化得到放大,二者在去除镜面反射后与ln(LAI)拟合模型的决定系数都达到0.6以上,因此,在植被覆盖较茂密时,SAVI指数和EVI指数更适合用于LAI反演。
SU Wei  XIE Zixuan  WANG Wei  JIN Tian  WANG Xinsheng
China Agricultural University
Key Words:unmanned aerial vehicle image  leaf area index  inversion  specular reflection removal  wavelet transform
Abstract:In order to solve the problem of accuracy of inversion of corn canopy LAI, it is necessary to study the effect of specular reflection on the image reflectance of unmanned aerial vehicle (UAV), which is independent of canopy structure. The wavelet transform was used to set the threshold of different bands of UAV image, and the specular reflection was weakened without affecting the diffuse reflection. The vegetation indices: NDVI, GNDVI, SAVI and EVI were constructed by using multi spectral UAV images of the Hebei Agricultural University Xinji Test Station acquired on July 15th and 26th, 2018. The single-variable inversion model of maize canopy LAI was constructed, and the accuracy of LAI inversion was evaluated by R2 and RMSE. The results showed that when the maize plants were sparse on July 15th, the R2 of vegetation indices and measured LAI after removing specular reflection were raised from 0.7190, 0.5598, 0.6241 and 0.5985 to 0.7633, 0.6940, 0.6497 and 0.6194, and the RMSE was also decreased from 0.2244, 0.2526, 0.2214 and 0.2245 to 0.1880, 0.1958, 0.1918 and 0.1987, which showed that removing specular reflection can improve the accuracy of LAI inversion. On July 26th, when the maize plants were relatively dense, the R2 of the four indices were also increased after the removal of specular reflection, which proved that the removal of specular reflection could improve the correlation between vegetation indices and LAI. However, in this case, NDVI and GNDVI tended to be saturated, and reducing the reflectivity by threshold method would aggravate the saturation phenomenon, so the two indices could not fully reflect the change of LAI. Meanwhile, SAVI and EVI were amplified by adding a canopy background adjustment factor, and their R2 of fitting model with ln(LAI) were both over 0.6 after removing specular reflection. Thus SAVI and EVI were more suitable for LAI inversion when vegetation cover was dense.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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