An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis
An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis
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DOI：
10.1109/jstars.2023.3243283
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发表时间：
2023
影响因子：
5.5
通讯作者：
Weixi Wang;Yuhang Fan;You Li;Xiaoming Li;Shengjun Tang
中科院分区：
文献类型：
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作者：
Weixi Wang;Yuhang Fan;You Li;Xiaoming Li;Shengjun Tang
基于三维移动的地图点云的道路树提取在智慧城市建设和高精度城市道路地图制作中具有重要作用。现有的方法往往是过度或欠分割分割重叠的街道树树冠和提取几何复杂的树木。为了解决这个问题，我们提出了一种基于改进的3-D形态学分析的方法，用于从移动的激光扫描仪（MLS）点云中提取街道树木。首先，利用基于深度学习的三维语义点云分割框架对原始点云进行预分类，得到场景中的植被点云。考虑到地形起伏的影响，对植被点云进行了确定，并通过高度空间滤波得到了包含树干的切片点云。在此基础上，采用基于体素的凸面积变化率约束的区域生长方法定位树。提出了一种渐进式树冠分割方法，该方法首先基于最小增量规则约束的基于体素的区域生长完成树冠点云的初步个体分割，然后采用基于“谷”结构的聚类优化树冠边缘。在这篇文章中，所提出的方法进行了验证，并使用从不同的情况下收集的三套MLS数据集的准确性进行评估。实验结果表明，该方法能有效地识别和定位不同几何形状的行道树，对树冠间附着力大的行道树具有较好的分割效果。树定位的准确率和召回率分别高于96.08%和95.83%，实例分割的平均精确率和召回率分别高于93.23%和95.41%。
Street tree extraction based on the 3-D mobile mapping point cloud plays an important role in building smart cities and creating highly accurate urban street maps. Existing methods are often over- or under-segmented when segmenting overlapping street tree canopies and extracting geometrically complex trees. To address this problem, we propose a method based on improved 3-D morphological analysis for extracting street trees from mobile laser scanner (MLS) point clouds. First, the 3-D semantic point cloud segmentation framework based on deep learning is used for preclassification of the original point cloud to obtain the vegetation point cloud in the scene. Considering the influence of terrain unevenness, the vegetation point cloud is deterraformed and slice point cloud containing tree trunks is obtained through spatial filtering on height. On this basis, a voxel-based region growing method constrained with the changing rate of convex area is used to locate the stree trees. Then we propose a progressive tree crown segmentation method, which first completed the preliminary individual segmentation of the tree crown point cloud based on the voxel-based region growth constrained by the minimum increment rule, and then optimizes the crown edges by “valley” structure-based clustering. In this article, the proposed method is validated and the accuracy is evaluated using three sets of MLS datasets collected from different scenarios. The experimental results show that the method can effectively identify and localize street trees with different geometries and has a good segmentation effect for street trees with large adhesion between canopies. The accuracy and recall of tree localization are higher than 96.08% and 95.83%, respectively, and the average precision and recall of instance segmentation in three datasets are higher than 93.23% and 95.41%, respectively.