Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves
文献类型： 外文期刊
作者： Bi, Kaiyi 1 ; Niu, Yifang 1 ; Yang, Hao 2 ; Niu, Zheng 1 ; Hao, Yishuo 1 ; Wang, Li 1 ;
作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词： multi-spectral point cloud; UAV; oblique photography; reflectance anisotropy; Maize
期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）
ISSN：
年卷期： 2025 年 17 卷 1 期
页码：
收录情况： SCI
摘要： Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman-Pinty-Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R2 = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R2 ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data.
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