光子计数激光雷达点云的自适应去噪算法

Adaptive Denoising Algorithm for Photon-Counting LiDAR Point Clouds

星载多波束光子计数激光雷达能实现高重复频率探测,有效提升了激光雷达在轨测量的空间分辨率,满足测绘和植被测量等应用需求。针对光子计数激光雷达点云的特征,提出了一种用于光子计数激光雷达点云的自适应去噪算法。首先,优化了搜索区域的形状,分析了噪声点邻域密度的分布特征。然后,根据噪声点的邻域密度统计特征自适应确定噪声点识别参数。对机载原理样机获取的点云数据实验结果表明,本算法对屋脊线的测量精度可达到0.13~0.27 m。对多测高波束试验激光雷达机载实验点云的实验结果表明,本算法对冰盖、海面、植被和陆地等典型场景的识别率优于94%,准确率优于90%。这表明本算法具有良好的适应性,可应用于大范围光子计数激光雷达点云的自适应去噪。

Spaceborne multibeam photon-counting LiDAR can achieve high repetition frequency detection,effectively improving the spatial resolution of LiDAR on-orbit measurements and meeting application requirements,such as surveying,mapping,and vegetation measurement.Aiming at the characteristics of photon-counting LiDAR point clouds,an adaptive denoising algorithm is proposed in this paper.First,the shape of the search area is optimized and the distribution characteristics of the neighborhood noise point density are analyzed.Then,identification parameters of the noise points are adaptively determined according to the statistical characteristics of the neighborhood noise point density.Experimental results of point cloud data obtained using the airborne prototype show that the measurement accuracy of the algorithm on roof ridge lines can reach 0.13-0.27 m.Experimental results of the multiple altimeter beam experimental LiDAR airborne experimental point cloud show that the recognition rate of the algorithm for typical scenes,such as ice sheet,sea surface,vegetation,and land,is better than 94%,and the accuracy rate is better than 90%.This shows that the algorithm has good adaptability and can be applied to adaptive denoising of large-scale photon-counting LiDAR point clouds.