[论文解读] A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery

Juheon Lee, David A. Coomes+7 more|arXiv (Cornell University)|Jan 24, 2017

Remote Sensing and LiDAR Applications参考文献 44被引用 7

一句话总结

本文提出一种3D树冠勾勒方法——多类别切割后接递归切割（MCRC），该方法在融合激光雷达点云与高光谱影像的基础上，利用图割算法提升树冠分割精度。通过采用鲁棒主成分分析（PCA）进行光谱特征降维，并结合带初始树冠先验的归一化切割，MCRC在高分辨率光学数据参与时，显著优于基于冠层高程模型（CHM）的方法，尽管计算成本较高，仍能实现精确的单株树木测量。

ABSTRACT

Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case). First, conventional methods are used to locate local maxima in the CHM and generate an initial map of trees. Second, a graph is built from the LiDAR point cloud, fused with the hyperspectral data. For computational efficiency, the feature space of hyperspectral imagery is reduced using robust PCA. Third, a multi-class normalised cut is applied to the graph, using the initial map of trees to constrain the number of clusters and their locations. Finally, recursive normalised cut is used to subdivide, if necessary, each of the clusters identified by the initial analysis. We call this approach Multiclass Cut followed by Recursive Cut (MCRC). The effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK. The performance of MCRC was usually superior to that of other delineation methods, and was further improved by including high-resolution optical imagery. Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual crown characteristics to be measured. By making full use of the data available, graph cut has the potential to considerably improve the accuracy of tree delineation.

研究动机与目标

• 解决基于冠层高程模型（CHM）的树冠勾勒中存在的平滑化与插值伪影导致的不准确性问题。
• 通过利用完整的3D激光雷达点云而非栅格化的CHM，提升单株树木检测能力。
• 利用鲁棒PCA实现高效光谱特征提取，将高光谱影像整合至3D树冠分割中。
• 开发基于图割的方法，保留树冠几何结构，实现对单株树冠特征的精确测量。
• 在多种森林类型中评估该方法的性能，并分析其局限性，尤其在检测下层林木方面。

提出的方法

• 从激光雷达点云构建冠层高程模型（CHM），以局部极大值作为初始树冠位置。
• 基于完整的3D激光雷达点云构建图结构，融合空间坐标与经鲁棒PCA降维的高光谱特征。
• 对图结构应用多类别归一化切割，利用初始树冠位置作为约束，确定聚类的数量与位置。
• 对每个聚类执行递归归一化切割，以细分可能过度分割的树冠，提升边界精度。
• 采用鲁棒PCA对高光谱数据进行降维，同时保留光谱信息并最小化光照影响。
• 对所有聚类固定递归切割的参数，尽管这可能限制其对复杂树冠结构的适应能力。

实验结果

研究问题

• RQ1 与基于CHM的方法相比，基于完整3D激光雷达点云并融合高光谱数据的图割方法是否能提升树冠勾勒精度？
• RQ2 高分辨率光学影像的整合在基于图割的3D树冠分割中对性能有何影响？
• RQ3 所提出的MCRC方法在多大程度上能够检测下层林木？激光雷达点云密度在此过程中起到何种作用？
• RQ4 采用鲁棒PCA进行光谱特征降维对分割的鲁棒性与计算效率有何影响？
• RQ5 递归归一化切割能否有效分割初始多类别切割步骤中识别出的大型或过度分割的树冠？

主要发现

• MCRC在三个测试数据集上均优于传统CHM方法：阿尔卑斯山的NewFor数据集、意大利阿尔卑斯山的针叶林，以及英国的落叶林。
• 高分辨率高光谱影像的引入显著提升了分割精度，尤其在结构复杂或混交林中表现更优。
• 仅在高密度激光雷达数据集（如意大利阿尔卑斯山站点）中成功检测到下层林木，因足够高的垂直分辨率揭示了亚冠层结构。
• 在低密度激光雷达数据中（如英国数据集，6个点/m²），由于点采样不足及潜在的位置不确定性，下层林木未被检测到。
• 通过将每个激光雷达回波分配至特定树木，该方法实现了精确的单株树木测量，支持冠幅参数的高精度估算。
• 尽管精度优异，MCRC计算成本较高，主要源于需反复求解特征系统，尤其在存在数千个初始树冠候选时更为显著。