辐射特征在LiDAR点云跨站点叶木分割中的作用
The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds
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中文总结 AI 辅助
本研究挑战LiDAR叶木分割中排除辐射特征的做法，证明跨站点泛化时辐射特征能显著提升木材召回率（119%）和结构连通性，优于仅几何方法。
中文摘要 AI 辅助
从LiDAR点云中对单棵树进行叶木分割对于用于非破坏性生物量估算的定量结构模型（QSMs）至关重要。现有分割方法通常排除辐射特征（如强度、回波次数）以最大化跨传感器兼容性。我们通过评估跨站点和跨平台泛化性来挑战这一设计选择：在公开的海德堡数据集（地面TLS，1550nm）上训练，并在来自加拿大安大略省的新数据集（RPA-LS，905nm）上进行测试。结果表明，仅基于几何的方法——包括在高密度LiDAR数据集上训练的最先进的深度学习模型——无法泛化到稀疏、自上而下几何结构的航空扫描，其F1分数≤0.56。纳入辐射特征（强度、回波次数、回波数量）将F1分数提升至0.61，但更重要的是，将木材召回率从0.16提高至0.35，增幅达119%。此外，仅基于几何的方法常常导致树干和树枝组件碎片化。我们发现，利用辐射特征能保留更大的结构连通性，从而产生更连贯的结构，更适合QSM重建。我们证明，虽然几何模式依赖于视角且容易过拟合扫描模式，但辐射特征编码了物理材料属性，这些属性能在不同传感器和环境之间泛化。
英文摘要
Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatibility. We challenge this design choice by evaluating cross-site and cross-platform generalization: training on the public Heidelberg dataset (terrestrial TLS, 1550nm) and testing on a novel dataset from Ontario, Canada (RPA-LS, 905nm). Results show that geometry-only methods - including state-of-the-art deep learning models trained on high-density LiDAR datasets - fail to generalize to the sparse, top-down geometry of aerial scans, achieving F1 scores <= 0.56. Incorporating radiometric features (intensity, return number, number of returns) improves F1 to 0.61, but more critically, increases wood recall by 119% from 0.16 to 0.35. Furthermore, geometry-only approaches often result in fragmented stem and branch components. We find that leveraging radiometric features preserves greater structural connectivity, resulting in more coherent architectures that are better suited for QSM reconstruction. We demonstrate that while geometric patterns are view-dependent and prone to overfitting scan patterns, radiometric features encode physical material properties that generalize across disparate sensors and environments.
发表机构
- University of Waterloo（滑铁卢大学）
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