Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models
文献类型： 外文期刊
作者： Yang, Weiguang 1 ; Fu, Huaiyuan 2 ; Xu, Weicheng 5 ; Wu, Jinhao 1 ; Liu, Shiyuan 2 ; Li, Xi 2 ; Tan, Jiangtao 2 ; Lan, Yubin 1 ; Zhang, Lei 2 ;
作者机构： 1.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou 510642, Peoples R China
2.South China Agr Univ, Coll Agr, Guangzhou 510642, Peoples R China
3.Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti, Guangzhou 510642, Peoples R China
4.Guangdong Lab Lingnan Modern Agr, Guangzhou 510642, Peoples R China
5.Guangdong Acad Agr Sci, Rice Res Inst, Guangzhou 510642, Peoples R China
关键词： multispectral imaging; data consistency; radiometric correction; Parrot Sequoia; DJI Phantom 4 Multispectral; data conversion modeling
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 12 期
页码：
收录情况： SCI
摘要： Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R2 values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms.
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