Impacts of Variable Illumination and Image Background on Rice LAI Estimation Based on UAV RGB-Derived Color Indices
文献类型： 外文期刊
作者： Sun, Binfeng 1 ; Li, Yanda 1 ; Huang, Junbao 1 ; Cao, Zhongsheng 1 ; Peng, Xinyi 1 ;
作者机构： 1.Jiangxi Acad Agr Sci, Inst Agr Engn, Nanchang 330200, Peoples R China
关键词： variable illumination; image background; color indices; Retinex correction; image segmentation
期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）
ISSN：
年卷期： 2024 年 14 卷 8 期
页码：
收录情况： SCI
摘要： Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation.
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