基于多尺度的城区机载点云滤波方法研究

摘要

To tackle the problems on the generation of Digital Elevation Model(DEM)in urban areas, this paper proposes lidar-driven multi-scale filtering algorithm. By analyzing and calculating spatial geometric relationships of each point in a certain neighbourhood it firstly classify point clouds into three basic classes(planar, edge, and discrete points). Thanks to urban areas featured by relatively flat piecewise planes, the planar points are further processed to obtain building, on-ground and uncertain points using region growing and global height thresholds. To improve the DEM accuracy, a Delaunay triangular network of the filtered on-ground points is built to backward search possible on-ground points in edge and uncertain points. The experiments of ISPRS datasets validate the effectiveness and robustness of the proposed coarse-to fine multi-scale filtering algorithm in urban areas.%针对城市地区机载激光扫描数据（ALS）中提取数字地面模型这一问题，提出了一种基于多尺度的由粗到细的滤波算法。通过计算每个点与其周围一定邻域内激光点的几何特征值关系，将点云粗分类为平面点、边缘点和离散点；对平面点进行区域跟踪，利用强度方差将平面点分类为地面点、建筑物点以及未确定类别点；对地面点构建Delaunay三角网，反向分析未确定点以及边缘点来加密地面点集。通过实验验证了该算法对城市地区滤波的有效性。