Single tree detection has been a major research topic when it comes to support of collecting automatic field inventory using lidar. All previous methods show under- and over-segmentation effects because the associated control parameters have a limited scope. This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. The key idea is to replace the static stopping criterion, which is usually defined by trial and error or by a sensitivity analysis, here with a query for whether a stem position has been provided by the stem detection in the remaining cluster to be partitioned. The stem detection automatically detects tree stems by identifying vertical lines based on a hierarchical classification procedure. We evaluate both stem detection and integrated single tree segmentation on mixed temperate forest plots captured in a leaf-on situation. The highly dense airborne lidar data was acquired with an average point density of more than 200 points/m². The forest areas, which are located in Eastern Austria, are characterized by a stem density of around 1000 stems/ha and a tree age between 15 and 63 years. We test our algorithms with reference data measured both by visual interpretation of the laser point clouds using a conventional field campaign. In our experiments, we confirm that the automatic stem detection technique successfully locates stems if the lidar point density of the stems is at least five points/m. The experimental results show that stem detection alone can detect more than 80% of the stems, with a precision of better than 70%. Moreover, we prove that the integration of the stem detection renders the graph-cut segmentation effectively independent of the stopping criterion and improves the overall accuracy of the tree segmentation as well. In terms of F-scores, the overall improvement is up to 15% and 6% for reference data from visual inspection and field measurements, respectively. Compared with the results of two existing tree segmentation methods that were applied to the same datasets, the accuracy improvement is also demonstrated by F-scores increased up to 22% and 5%, respectively. Very interestingly, the integrated tree segmentation considerably enhances the detection accuracy, especially in mixed and deciduous forest areas by more than 10% in the case of reference data provided by visual inspection.

当支持使用激光雷达收集自动现场清单时，单树检测一直是主要的研究主题。由于关联的控制参数范围有限，所有以前的方法都显示分段不足和分段过度的效果。本文介绍了一种新的基于图割聚类的集成单树分割方法，该方法支持自动词干检测。关键思想是替换静态停止标准，该标准通常是通过反复试验或通过敏感性分析来定义的，这里用一个查询来确定是否在剩余的待划分簇中通过词干检测提供了词干位置。茎检测通过基于分层分类程序识别垂直线来自动检测树茎。我们评估在叶上捕获的混合温带森林地块上的茎检测和综合单树分割。采集的高密度机载激光雷达数据的平均点密度超过200个点/米²。位于奥地利东部的森林地区的特点是茎密度约为1000茎/公顷，树龄在15至63岁之间。我们使用参考数据测试算法，这些参考数据都是通过使用常规野战活动对激光点云进行视觉解释而测得的。在我们的实验中，我们确认，如果茎的激光雷达点密度至少为5个点/米，则自动茎检测技术将成功定位茎。实验结果表明，单独的茎检测可以检测出80％以上的茎，其准确度优于70％。此外，我们证明了词干检测的集成使图割分割有效地独立于停止准则，并且还提高了树分割的整体准确性。在F分数方面，目视检查和现场测量的参考数据的总体改进分别高达15％和6％。与应用于相同数据集的两种现有树分割方法的结果相比，F分数分别提高了22％和5％，也证明了准确性的提高。非常有趣的是，集成树分割显着提高了检测精度，特别是在通过目视检查提供参考数据的情况下，在混合和落叶林地区，检测精度提高了10％以上。分别。非常有趣的是，集成树分割显着提高了检测精度，特别是在通过目视检查提供参考数据的情况下，在混合和落叶林地区，检测精度提高了10％以上。分别。非常有趣的是，集成树分割显着提高了检测精度，特别是在通过目视检查提供参考数据的情况下，在混合和落叶林地区，检测精度提高了10％以上。