机载激光雷达系统可以直接有效地获取地物的三维点云信息,为数字高程模型生成和建筑物检测与三维重建提供强有力的数据保障,但原始点云数据不可避免地会产生噪声点。提出了一种基于多窗口顶帽变换的机载激光雷达点云噪声去除算法。首先,根据原始点云间隔对点云进行内插生成网格,分别获取了最大和最小网格数据;然后,对网格数据进行聚类,根据每个聚类区域的尺寸设置阈值,检测出初始噪声点所在网格区域;最后,利用白、黑顶帽变换理论分别对最大和最小网格数据进行处理,检测出最终的噪声点所在网格区域。利用ISPRS数据对此方法和其他方法进行对比实验分析,结果表明,本文方法不仅可以较好地去除噪声点,还可以较完整地保存原始点云的细节信息。

Airborne laser radar (LiDAR) system can directly and effectively obtain three-dimensional point cloud information of ground features, to provide powerful data guarantee for the generation of digital elevation model, building detection and three-dimensional reconstruction. However, the original point cloud data will inevitably produce noise points. A method of noise removal for airborne LiDAR point cloud based on the multi-window top-hat transformation is proposed. The grid interpolation is performed on the point cloud according to the interval of point cloud to obtain the maximum and minimum grid data, respectively. The grid data is clustered, and the original noise areas are detected by setting the area size threshold. The maximum and minimum grids are processed using the white and black top-hat transformation theory respectively to detect the final grid area where noise points are located. The method is compared and analyzed with other methods based on the ISPRS data. The results show that the proposed method can remove the noise points, and completely preserve the details of the original point cloud.