结合改进DBSCAN和统计滤波的单光子去噪算法

Single photon denoising algorithm combined with improved DBSCAN and statistical filtering

为了解决光子计数激光雷达探测数据中噪声点云过多的问题,采用结合基于密度的噪声空间聚类应用算法(DBSCAN)和统计滤波算法的单光子点云去噪方法,以美国国家航空航天局提供的多波束试验激光雷达实际飞行数据为实验数据,通过k维树求取点云密度进行粗去噪,然后运用改进DBSCAN算法和统计滤波算法进行精去噪,进行了理论分析和实验验证。结果表明,实验区目标点云识别率在85%以上,性能优于经典的半径滤波算法。这一结果对于光子数据去噪是有帮助的。

In order to solve the problem of excessive noise point clouds in the photon counting lidar detection data,a single photon point cloud denoising method based on a combination of improved density-based spatial clustering of applications with noise(DBSCAN)algorithm and statistical filtering algorithm was adopted.The actual flight data of multiple altimeter beam experimental lidar provided by National Aeronautics and Space Administration was experimental data.First,the point cloud density was obtained through the k-dimensional tree for rough denoising,and then the improved DBSCAN algorithm and statistical filtering algorithm were used for fine denoising.The theoretical analysis and experimental verification has achieved good results.The results show that the target point cloud recognition rate in the experimental area is above 85%,and the performance is better than the classic radius filtering algorithm.This result is helpful for photon data denoising.