机载LiDAR点云数据滤波是LiDAR数据后处理过程中的关键步骤。在分析三角网滤波与曲面拟合滤波特点的基础上, 提出了一种“由粗到精”的处理思想用于LiDAR点云数据滤波。该方法通过强阈值三角网算法进行II类误差优先的“粗分类”, 获取可靠性较高的初始地面点, 以“粗分类”结果作为先验信息进行种子点选取, 引入总体最小二乘算法完成曲面拟合, 设置自适应阈值实现不同区域灵活处理, 最终得到较为精细的地面模型。使用ISPRS测区数据及Niagara数据进行实验, 与经典滤波算法及传统曲面拟合方法进行对比, 实验结果证明, 该方法较传统算法能够得到更加可靠的滤波结果, 对各种地形的适应性较强, 具备较高的实用价值。

Airborne LiDAR point cloud data filtering is the most important step in the workflow of LiDAR data postprocessing. Based on the characteristics of Triangular Irregular Networks(TIN) and curved fitting filtering methods, a "from rough to fine" idea was proposed for LiDAR point cloud data filtering. In this method, strict threshold TIN was used for "rough classification" with a priority of type II error and more reliable initial ground points were obtained, then the seed points were selected with the priori information which was "rough classification" result, next Total Least Squares(TLS) algorithm was introduced to fit block terrain, and self-adaption threshold was set to deal with different area more flexibly, ultimately more refined region model was obtained. ISPRS test data and Niagara data were used for experiments, and classic filtering method and traditional curved fitting filtering method were selected for comparison. Experimental results prove that, the proposed method is practical as the filtering results are more reliable than traditional moving curved fitting filtering method, and has strong adaptability to various terrains.