LiDAR (Light Detection and Ranging) has become an essential part of the remote sensing toolbox used for biosphere monitoring. In particular, LiDAR provides the opportunity to map forest leaf area with unprecedented accuracy, while leaf area has remained an important source of uncertainty affecting models of gas exchanges between the vegetation and the atmosphere. Unmanned Aerial Vehicles (UAV) are easy to mobilize and therefore allow frequent revisits to track the response of vegetation to climate change. However, miniature sensors embarked on UAVs usually provide point clouds of limited density, which are further affected by a strong decrease in density from top to bottom of the canopy due to progressively stronger occlusion. In such a context, discriminating leaf points from wood points presents a significant challenge due in particular to strong class imbalance and spatially irregular sampling intensity. Here we introduce a neural network model based on the Pointnet ++ architecture which makes use of point geometry only (excluding any spectral information). To cope with local data sparsity, we propose an innovative sampling scheme which strives to preserve local important geometric information. We also propose a loss function adapted to the severe class imbalance. We show that our model outperforms state-of-the-art alternatives on UAV point clouds. We discuss future possible improvements, particularly regarding much denser point clouds acquired from below the canopy.

翻译：激光雷达已成为生物圈监测遥感工具箱的重要组成部分。具体而言，激光雷达能够以前所未有的精度绘制森林叶面积图，而叶面积一直是影响植被与大气间气体交换模型不确定性的重要来源。无人机易于部署，因此可频繁重访以追踪植被对气候变化的响应。然而，无人机搭载的小型传感器通常仅能提供密度有限的点云，且因冠层自顶向下的遮蔽效应逐渐增强，其密度会急剧下降。在此背景下，区分叶点与木质点面临显著挑战，尤其表现为严重的类别不平衡和空间不规则采样强度。本文提出一种基于PointNet++架构的神经网络模型，仅利用点几何信息（不含光谱数据）。为应对局部数据稀疏性，我们设计了一种创新采样方案，力求保留局部重要几何信息；同时提出适应严重类别不平衡的损失函数。实验表明，该模型在无人机点云上优于现有最优方法。最后讨论了未来可能的改进方向，特别是针对从冠层下方获取的更高密度点云。