3D Segmentation of Trees Through a Flexible Multiclass Graph Cut Algorithm
3D Segmentation of Trees Through a Flexible Multiclass Graph Cut Algorithm
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DOI：
10.1109/tgrs.2019.2940146
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发表时间：
2019-03
影响因子：
8.2
通讯作者：
Jonathan Williams;C. Schönlieb;T. Swinfield;Juheon Lee;Xiaohao Cai;L. Qie;D. Coomes
中科院分区：
文献类型：
--
作者：
Jonathan Williams;C. Schönlieb;T. Swinfield;Juheon Lee;Xiaohao Cai;L. Qie;D. Coomes
开发一种强大的算法，用于根据机载激光扫描 (ALS) 数据集自动检测单个树冠 (ITC)，对于跟踪树木对人为变化的响应非常重要。这些方法可以测量单棵树木的大小、生长和死亡率，从而跟踪和了解森林碳储量和动态。对于结构简单的森林（包括针叶林和种植园）存在许多算法。为结构复杂、物种丰富的热带森林寻找强有力的解决方案仍然是一个挑战；在估计地块级生物量时，现有的分割算法通常表现不如简单的基于区域的方法。在这里，我们描述了一种用于树冠描绘的多类图割（MCGC）方法。它使用本地 3D 几何和密度信息以及牙冠异速知识，从机载光检测和测距点云中分割 ITC。我们的方法可以稳健地识别树冠顶部和中间层的树木，但无法识别小树。通过这些 3D 树冠，我们能够测量单棵树的生物量。将这些估计值与永久清查地块的估计值进行比较，我们的算法可以对公顷规模的碳密度进行可靠的估计，证明了 ITC 方法在监测森林方面的强大功能。我们的方法可以灵活地添加额外的信息维度（例如光谱反射率），使该方法成为未来开发和扩展到其他 3D 数据源（例如运动数据集的结构）的明显途径。
Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.