首页|基于多约束图形分割的点云对象基元获取方法

基于多约束图形分割的点云对象基元获取方法

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针对目前LiDAR点云对象基元获取方法存在的运算量大、不能对建筑物不同屋顶平面进行有效分割等问题,提出一种基于多约束图形分割的点云对象基元获取方法.该方法采用基于图的分割策略,首先使用邻近点约束条件构建网图结构,以此来降低图的复杂度,提高算法的实现效率;然后对相邻节点的法向量夹角进行阈值约束,从而将位于同一平面的点云分割为同一对象基元;最后进行最大边长约束,对建筑物点云与其邻近的植被点进行分离.为验证所提方法的有效性,选用 3组由国际摄影测量与遥感学会(ISPRS)提供的公开测试数据集进行测试以及 2组由武汉大学提供的数据集进行实验分析.实验结果表明,所提方法能够有效分割建筑物的不同屋顶平面.使用DBSCAN和谱聚类方法与所提方法进行对比,利用准确率、召回率和F1 得分作为精度评价指标.相比其他方法,在 5组不同建筑物环境的点云数据中,所提方法均能取得最佳的整体分割效果,召回率和F1得分均优于其他两种方法.
LiDAR Point Object Primitive Obtaining Based on Multiconstraint Graph Segmentation
A LiDAR point object primitive obtaining method still encounters challenges,such as large computation amount and ineffective segmentation for different building roof planes.A point object primitive obtaining method based on multiconstraint graph segmentation is proposed to address these challenges.A graph-based segmentation strategy is adopted for this method.First,constraint conditions of adjacent points are used to construct a network graph structure to reduce the complexity of the graph and improve the efficiency of the algorithm.Subsequently,the angle of the normal vectors of adjacent nodes is constrained using a threshold value to divide the point cloud located in the same plane into the same object primitive.Finally,the maximum side length constraint is performed to separate the building point cloud from its adjacent vegetation points.Three sets of public test data provided by the International Society for Photogrammetry and Remote Sensing(ISPRS)and two datasets located in Wuhan University were selected for testing to verify the validity of the proposed method.Experimental results show that the proposed method can effectively divide different roof planes of buildings.DBSCAN and spectral clustering methods were used for comparison,and precision,recall,and F1 score were adopted as evaluation indexes.Compared with the other two methods,the proposed method achieves the best overall segmentation results in case of the five datasets with different building environments,with better recall and F1 score.

airborne LiDARpoint cloudobject primitivegraph segmentationnormal vector constrain

惠振阳、李卓宣、程朋根、蔡诏晨、郭先春

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东华理工大学测绘与空间信息工程学院,江西 南昌 330013

东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室,江西 南昌 330013

机载LiDAR 点云 对象基元 图形分割 法向量约束

国家自然科学基金国家自然科学基金中国博士后科学基金江西省自然科学基金江西省教育厅科技项目江西省数字国土重点实验室开放基金东华理工大学博士启动基金

42161060418013252019M66185820192BAB217010GJJ170449DLLJ201806DHBK2017155

2024

10.3788/LOP231575

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子：1.153
ISSN：1006-4125
年,卷(期)：2024.61(10)
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