可变照明对植被指数的影响,以及评估照明校正方法对使用无人机图像估计的叶绿素含量的影响
Yuxiang Wang1,2, Zengling Yang3, Gert Kootstra4
1College of Engineering, China Agricultural University, Beijing, China. wangyuxaing@cau.edu.cn.
Plant methods
|May 27, 2023
概括
照明校正对于使用无人机植被指数 (VIs) 进行准确的作物监测至关重要. 像Retinex这样的方法可以在可变的照明下改善叶绿素估计,从而提高精确农业.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 无人驾驶飞行器 (UAV) 技术通过植被指数 (VI) 提供精确的作物监测.
- 场景照明的变化会影响VI的准确性和衍生的作物健康指标,如叶绿素含量.
- 开发照明不变的VI对于可靠的农业评估至关重要.
研究的目的:
- 评估各种植被指数 (VIs) 用于在不同的照明条件下监测作物.
- 评估照明校正方法,包括经验线方法 (ELM) 和Retinex算法.
- 与现场测量相比,对正确的VI在估计叶子叶绿素含量的性能进行比较.
主要方法:
- 在阳光,多云和部分云层条件下收集无人机图像.
- 应用实证线方法 (ELM) 用于使用参考面板进行放射学校准.
- 实现了多级Retinex算法用于在线照明校正.
- 计算了植被指数 (VIs),并使用它们来预测叶子的叶绿素含量.
主要成果:
- 经验线方法 (ELM) 在可变的,部分阴天条件下显示性能下降.
- 植被指数 (VIs) 预测了叶绿素含量,R2值为0.6 (阳光明) 和0.56 (多云).
- Retinex算法在可变照明下 (R2=0.61) 证明了在叶绿素估计中的卓越性能,优于其他方法.
结论:
- 照明校正显著提高了植被指数 (VIs) 的表现.
- 精确的叶绿素含量估计依赖于强大的VI,特别是在波动的光线下.
- 在Retinex算法证明有效的照明不变作物监测与无人机.
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