基于一体化聚类滤波的机载LiDAR数据DTM提取

提出一种一体化聚类的滤波方法，从机载激光扫描数据中获取复杂城区的DTM。通过对激光点云数据构建八叉树，以节点满足平面判断条件进行双重距离聚类，对剩余点引入渐进三角网加密方法进行迭代判断，进而有效区分地面点和非地面点。最后，将本文滤波方法与经典滤波进行比较，得到可靠的DTM，验证了本文方法的有效性。

Integrative Clustering Filtering of Airborne LiDAR Data forDigital Terrain Model Generation

This paper presents an efficient method, based on integrative clustering filtering (ICF), for an accurate terrain assessment in complex areas. Firstly, the point cloud data is described as the Octree index structure, and then segmented based on planarity. Then a coarse spatial clustering process, based on dual distance is implemented. Secondly, a triangulated irregular network (TIN) is built based on the classic progressive densification method, and the rest of the valid point clouds are classified iteratively. Finally, the experimental results show that the ICF method proposed in this paper is capable of producing reliable DTMs in the discontinuous areas, and all of the above contribute to improving the reliability of the filtering result.