综合不规则三角网和区域生长的建筑物提取方法

应用激光雷达探测技术(LiDAR)进行建筑物提取其效率一直是工程应用的关键,针对现有先滤波后提取建筑物一类方法效率低下的问题,提出一种综合不规则三角网和区域生长的从原始机载激光雷达数据中直接提取建筑物的方法。首先利用原始点云数据建立不规则三角网,利用三角网中突起物边缘点所在三角形的法向量、边长及高程特征,提取突起物边缘点;然后以提取出的边缘点为种子点,根据三角网连接关系进行区域生长,提取突起物点集合;最后删除集合中点数量较少的非建筑物点集,得到建筑物点集。该方法可直接从原始点云数据中提取出不同建筑物的点集,无需经过滤波操作。通过仿真实验证明该方法在保证建筑物提取准确度的情况下效率有明显提高并且具有一定的适用性。

A Building Extraction Method on Comprehensive Delaunay TIN Models and Region Growing

The efficiency of the extracted buildings always is a key in engineering application of LiDAR points. Aimed at the problem that the efficiency of filtering first and then extracting methods, currently in effect is low, a method combining Delaunay TIN models and region growing for extracting buildings from raw LiDAR data is put forward in this paper. Firstly, Delaunay TIN models are built on the original LiDAR points. Edge points of buildings can be extracted by using the normal vector, length of side and point height of triangles where the edge points are located. Then, the extracted edge points are assigned as seed points in order to implement region growing based on triangle network connections which will yield a points set of protrusion. Finally, since the number of non-building points is much smaller than that of the building points, the non-building points set can be deleted while the building points set is reserved. The method in this paper can be used to extract building points set and edge points directly without the operation of filtering and provide foundation for further contour extracting and building reconstruction. The simulation results show that the method has obvious efficiency under the guarantee of accuracy in extraction and has a certain of adaptability.