点云数据滤波是机载激光雷达(LiDAR)数据处理研究中很重要的问题之一。提出了一种基于区域预测的LiDAR 点云数据形态学滤波算法，该算法由点云数据建立规则格网、去除粗差点，对实验区域进行分块，然后使用各个分块区域的高程标准差预测地形坡度参数s 从而进行渐进式形态学滤波，最终确定地面点。文中算法优点在于可以根据区域地形起伏情况自适应地得到阈值进行滤波。使用国际摄影测量与遥感学会(ISPRS)提供的测试数据进行实验，实验结果表明，提出的算法能够有效去除地物点和保留地面点，并且能有效降低总误差。

The point cloud data filtering is always an important problem in the research of airborne LiDAR data. A LiDAR point cloud data filtering algorithm based on region prediction is proposed. The method creates a regular grid with point cloud data and removes outliers, divides the experimental area into different blocks and uses sub-blocks′ elevation standard deviation to predict the terrain slope parameters, finally determines the ground points. The proposed algorithm has an advantage of obtaining threshold adaptively by the conditions of topographic relief of the region. The international society for photogrammetry and remote sensing (ISPRS) reference dataset is used to test the method. The experimental results show that the proposed method can effectively remove non-ground points, keep the ground points and is effective at minimizing total error rates.