An Efficient Downwelling Light Sensor Data Correction Model for UAV Multi-Spectral Image DOM Generation
文献类型： 外文期刊
作者： Wu, Siyao 1 ; Lu, Yanan 1 ; Fan, Wei 1 ; Zhang, Shengmao 1 ; Wu, Zuli 1 ; Wang, Fei 1 ;
作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Fisheries Remote Sensing, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China
关键词： rotary-wing UAV; DLS; reflectance; curve fitting
期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）
ISSN：
年卷期： 2025 年 9 卷 7 期
页码：
收录情况： SCI
摘要： The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach-FIM-DC (Fitting and Interpolation Model-based Data Correction)-specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions.
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