无人机多源数据融合具有超级分辨率,用于准确估计大豆叶面积指数
Zhenqing Zhao1,2, Huabo Yao1,2, Depeng Zeng2,3
1College of Electrical Engineering and Information, Northeast Agricultural University, Harbin, China.
Frontiers in plant science
|December 8, 2025
概括
超分辨率 (SR) 图像重建与多传感器数据相结合,改善了大豆的叶面积指数 (LAI) 估计. 这种方法提高了精度,尽管无人机飞行高度各不相同,这有利于精准农业.
科学领域:
- 农业遥感 农业遥感
- 计算机视觉 计算机视觉
- 生物物理参数估计估计
背景情况:
- 叶面积指数 (LAI) 对于作物健康评估至关重要.
- 无人驾驶飞行器 (UAV) 提供高效的作物监测,但面临与高度相关的准确性挑战.
- 将超分辨率 (SR) 与多传感器数据集成可能可以克服这些局限性.
研究的目的:
- 调查SR图像重建的有效性与大豆LAI估计的多传感器数据相结合.
- 评估不同无人机飞行高度对LAI估计准确性的影响.
- 为了比较各种SR算法和数据融合策略.
主要方法:
- 在多个无人机高度 (15m-60m) 捕获RGB和多光谱图像.
- 应用SR算法 (SwinIR,Real-ESRGAN,SRCNN,EDSR) 进行图像增强.
- 提取了纹理特征,并使用XGBoost与数据融合 (RGB,多光谱,组合) 开发了LAI估计模型.
主要成果:
- 斯威尼尔IR展示了优越的SR性能;SR有效性随着高度的增加而下降.
- 与XGBoost合并的RGB多谱数据产生了最高的精度 (4.16%的相对误差).
- 在30米 (R2=0.86) 和45米 (R2=0.77) 的高度上,SR显著提高了准确性.
结论:
- 与多传感器数据集成的SR图像重建有效地减轻LAI估计在更高的UAV高度的精度损失.
- 这种综合方法为精准农业和无人机作物监测提供了强大的框架.
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