In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

在这项研究中，我们提出了一种通过应用专门的约束条件随机场（CRF）来提高ALS数据中单棵倒下的树茎自动分割质量的方法。整个处理流程包括两个步骤。首先，检测长度相等的短茎节，并选择它们的子集进行进一步处理，而在第二步中，将选定的节合并以形成整棵树。第一步是使用在段标签空间上定义的专用CRF来完成的，该CRF能够找到易于随后合并的段候选。为了实现这一目标，CRF不仅单独考虑每个候选对象的特征，而且还将相邻段之间的成对空间相互作用纳入模型中。尤其是，成对相互作用包括从训练数据以及空间重叠率获悉的共线性/角度偏差概率，而一元电势则编码每个段周围激光点分布的获悉概率模型。这些组件中的每一个都以自己的平衡因子输入CRF能量。为了处理以前看不见的数据，我们首先通过最小化CRF能量来计算要合并在平衡因子网格上的分段子集。然后，我们执行合并，并根据从学习的树外观模型获得的合并树的质量对平衡配置进行排序。最终结果来自排名靠前的配置。我们使用从田野调查中获得的参考数据，对来自巴伐利亚森林国家公园的5个样地进行了测试。