机载LiDAR点云分块插值滤波

现有机载激光雷达（LiDAR）点云滤波算法在简单地形下取得了较好的滤波效果，但普遍对陡坡地形适应性较差。为提高在不同地形下的滤波性能，提出了基于分块的多尺度表面插值滤波算法。该算法首先通过改进的区域增长分块算法将原始点云分为点云块集和散点集，然后通过构建的多尺度表面插值算法同时对点云块和散点进行分类。利用国际摄影测量与遥感学会(ISPRS)提供的基准数据验证表明，该方法在15个样本中有11个样本滤波效果优于现有滤波方法，对各类地形均有较强适应性，且该方法平均总误差最小。对三种不同地形特征的高密度数据滤波实验，也验证了该方法的良好性能。

Interpolation-based filtering with segmentation for airborne LiDAR point clouds

The classical airborne LiDAR filtering algorithms show good results on most landscapes, but they suffer from low-level adaptability on steep slopes. Thus, to improve the filtering performance under different environments, a surface interpolation-based filtering algorithm with segmentation was proposed. Firstly, the original point clouds were grouped into a set of segments and one set of scattered points by an improved region growing method. Then, the segments and the scattered points were classified simultaneously using a weighted least square algorithm. The benchmark dataset provided by International Society for Photogrammetry and Remote Sensing(ISPRS) was used to validate the performance of the proposed method. Results illustrate that the proposed method outperforms the state-of-the-art filtering methods on 11 out of 15 samples, showing its strong adaptability to different terrain environments. Moreover, the proposed method has the lowest average total error. Filtering three samples of high-density with different terrain features also demonstrates the promising performance of the proposed method.