首页|基于改进DBSCAN的星载激光雷达数据多尺度滤波研究

基于改进DBSCAN的星载激光雷达数据多尺度滤波研究

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星载激光雷达数据滤波过程易受复杂背景、粗差点、噪声点等问题的干扰，导致滤波效果大幅度下降，所以研究基于改进DBSCAN的星载激光雷达数据多尺度滤波方法。采用改进DBSCAN算法对星载激光雷达数据做聚类处理，并标记噪声点，通过半球形邻域算法提取点云数据特征。根据提取到的点云数据特征构建规则格网，通过格网的多路径效应剔除点云数据中的粗差点与噪声点，完成星载激光雷达数据多尺度滤波。实验结果表明，所提方法的星载激光雷达数据多尺度滤波误差较低、滤波效果好，实际应用价值较高。
Multi-scale filtering of spaceborne lidar data based on improved DBSCAN
The filtering process of spaceborne LiDAR data is susceptible to interference from complex back-grounds,gross errors,noise points,and other issues,resulting in a significant decrease in filtering effectiveness.Therefore,a multi-scale filtering method for spaceborne LiDAR data based on improved DBSCAN is studied.The im-proved DBSCAN algorithm is used to cluster spaceborne LiDAR data,label noise points,and extract point cloud data features using a hemispherical neighborhood algorithm.Based on the extracted point cloud data features,a regular grid is constructed,and the coarse points and noise points in the point cloud data are removed through the multipath effect of the grid,completing multi-scale filtering of spaceborne LiDAR data.The experimental results show that the pro-posed method has low multi-scale filtering error and good filtering effect for spaceborne LiDAR data,and has high practical application value.

improve DBSCANspaceborne lidarmulti scale filteringhemispherical neighborhood algorithmregular gridrough handicapnoise point

钱政、毛志华、姚宝恒

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上海交通大学海洋学院,上海 201100

自然资源部第二海洋研究所卫星海洋环境动力学国家重点实验室,杭州 310012

改进DBSCAN 星载激光雷达 多尺度滤波 半球形邻域算法 规则格网 粗差点 噪声点

国家重点研发计划支持项目上海交通大学深蓝计划自然资源部第二海洋研究所基本科研业务费专项国家自然科学基金

2016YFC1400901SL2022ZD206SL230261991454

2024

10.14016/j.cnki.jgzz.2024.04.154

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子：0.74
ISSN：0253-2743
年,卷(期)：2024.45(4)
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