基于形态学与区域生长的机载LiDAR点云数据滤波

针对形态学滤波存在的地形过度腐蚀以及区域生长滤波需要大量地面种子点的问题,提出一种点云数据由粗到精的两级滤波策略。对机载LiDAR点云数据进行多尺度形态学滤波得到粗略DEM("粗滤波"),由此可提供大量的地面种子点用于区域生长,进而得到精细DEM("精滤波")。利用ISPRS提供的滤波数据进行测试,结果表明,该滤波算法对地形适应性较强,可有效保证地形特征,具有更好的稳健性。

Filtering of Airborne LiDAR Point Cloud Data Based on Mathematical Morphology and Region Growing

According to the phenomenon that a morphological filter erodes terrain excessively and the demand of seed ground points for region growing, a two-stage “rough-refined” filtering strategy is proposed. First, a rough digital elevation model (DEM) is obtained by applying a multi-scale morphological filter to airborne LiDAR data, which is called rough filter. Then, region growing is applied to gain refined DEM based on the seed ground points derived from the rough DEM. When tested against the ISPRS LiDAR reference datasets, the experiment shows that the proposed algorithm is highly adaptable to various landscapes in reserving the detail of terrain effectively, and also is more robust.