融合形态学灰度重建与三角网分层加密的LiDAR点云滤波

Integrating Morphological Grayscale Reconstruction and TIN Models for High-quality Filtering of Airborne LiDAR Points

形态学滤波与三角网加密滤波是从LiDAR点云中自动识别真实地面点的两种重要方法,本文分析了两种方法优劣性及其过程实施的特点,提出了一种融合形态学灰度重建与不规则三角网分层加密的点云滤波新策略:1首先对LiDAR点云实施Ⅰ类错误优先的形态学灰度重建初始滤波,并通过"非最小值抑制"将LiDAR点云标记为地面可靠点、地面可疑点、非地面可疑点三种类别;2依据形态学灰度重建迭代顺序对非地面可疑点进行分层标记;3利用地面可靠点构建初始三角网,对地面可疑点、非地面可疑点依次进行三角网加密滤波,并基于分层标记信息自适应调整地面点判据参数。ISPRS标准数据滤波实验结果表明,本方法滤波质量高且具有较好的通用性。

Based on the characteristics of the morphological filter and the TIN-based progressive filter, a high-quality LiDAR point cloud filtering algorithm combining Morphological grayscale reconstruction and TIN Models is proposed in this paper. Its main strategies are. （1）Implementing morphological grayscale reconstruction with a priority of Type I Error and non-minimum suppression. In this step, LiDAR point clouds are tagged as Reliable terrain points G, suspicious terrain points S and suspicious Non-terrain points NG; （2）Suspicious non-terrain points are further tagged based on the iterative order of Morphological grayscale reconstruction. In this step, small and constant height interval is used to filter the possible non-terrain points at different elevation; （3）Constructing the initial TIN from points G and further filtering points S and NG points, respectively, by adaptively adjusting the parameters of the ground point criterion at associated point layer. We did an experiment with 15 ISPRS test data sets and assessed the results with the standard criterion as found in the literature. The result shows that proposed filtering algorithm dramatically improved filtering quality, even for complex terrain.