精确的图像色彩校正基于双人无人机合作飞行
Xuqi Lu1,2, Jiayang Xie1,2, Jiayou Yan3
1State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, PR China.
Plant phenomics (Washington, D.C.)
|December 19, 2025
概括
本研究介绍了一种合作的双无人飞行器 (UAV) 飞行方法,采用彩色图表进行飞行中的RGB图像校正. 这种新的方法显著提高了植物健康监测的遥感颜色精度和一致性.
科学领域:
- 遥感和地理空间技术 遥感和地理空间技术
- 农业科学和作物监测 农业科学和作物监测
- 图像处理和计算机视觉
背景情况:
- 精确的色彩遥感对于植物健康监测,生长阶段识别和压力检测至关重要.
- 由于照明和传感器变化的未经纠正的色彩扭曲会损害图像数据质量和分析可靠性.
- 现有的色彩校正方法通常需要后处理,限制实时应用.
研究的目的:
- 引入和验证一种用于RGB图像的新型飞行颜色校正方法,使用配合双无人机 (UAV) 飞行与彩色图 (CoF-CC) 集成.
- 为了提高色彩精度和一致性,在不同的场景条件下,远程传感图像.
- 证明 CoF-CC 方法在改进作物监测应用,如大米成熟度估计方面的实际实用性.
主要方法:
- 开发了一个合作的双无人机系统,其中一个无人机 (主机) 获取RGB图像,而第二个无人机则携带ColorChecker图表.
- 确保 ColorChecker 图表在主无人机视野内的持续可见性,以便实时计算颜色校正矩阵 (CCM).
- 应用CCM对RGB图像进行飞行校正,随后进行实地实验,以评估跨传感器一致性和在米叶上的测量准确性.
主要成果:
- CCM显著减少了CIE 2000年平均颜色差异 (ΔE) 的66.1%,将其从18.2降至5.0的米叶颜色.
- 六个RGB传感器的颜色一致性提高了70.2%,并显著增加了集群内距离.
- 经过校正的图像大大提高了大米成熟度预测的准确性,将R2从0.28增加到0.67.
结论:
- 配合色图 (CoF-CC) 方法的合作双无人机飞行有效地在不同的照明和传感器条件下标准化RGB图像.
- 这种方法证明了在开放场外遥感应用中准确的色彩分析和解释的强大性能.
- CoF-CC为提高无人机作物监测和分析的可靠性和准确性提供了一个实用的解决方案.
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