顾及地形特征和边界防收缩的机载LiDAR地面点云聚类简化方法

武慧明; 陈传法; 孙延宁; 郭娇娇; 贝祎轩

doi:10.11834/jrs.20233032

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顾及地形特征和边界防收缩的机载LiDAR地面点云聚类简化方法
Airborne LiDAR Point Cloud Clustering Simplification Algorithm Considering Terrain Features and Boundary Protection against Contraction
2023年页码：1-16
网络出版日期： 2023-11-29 ，
DOI： 10.11834/jrs.20233032

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武慧明，陈传法，孙延宁，郭娇娇，贝祎轩.XXXX.顾及地形特征和边界防收缩的机载LiDAR地面点云聚类简化方法.遥感学报，XX（XX）： 1-16

WU Huiming，CHEN Chuanfa，SUN Yanning，GUO Jiaojiao，BEI Yixuan. XXXX. Airborne LiDAR Point Cloud Clustering Simplification Algorithm Considering Terrain Features and Boundary Protection against Contraction. National Remote Sensing Bulletin， XX（XX）：1-16
武慧明，陈传法，孙延宁，郭娇娇，贝祎轩.XXXX.顾及地形特征和边界防收缩的机载LiDAR地面点云聚类简化方法.遥感学报，XX（XX）： 1-16 DOI： 10.11834/jrs.20233032.

WU Huiming，CHEN Chuanfa，SUN Yanning，GUO Jiaojiao，BEI Yixuan. XXXX. Airborne LiDAR Point Cloud Clustering Simplification Algorithm Considering Terrain Features and Boundary Protection against Contraction. National Remote Sensing Bulletin， XX（XX）：1-16 DOI： 10.11834/jrs.20233032.

摘要

点云简化是海量机载LiDAR地面点云高效传输和多尺度应用的前提。针对目前地面点云简化方法存在复杂环境适用性差、地形细节特征丢失等问题，本文提出了一种顾及地形特征和边界防收缩的机载LiDAR点云聚类简化算法。首先利用K-means算法将点云分割为初始点云簇，然后依据各簇的地形复杂度再次对其细分，接着借助点云法向量信息以及邻接簇间边缘点的高程差识别地形特征点，最后通过保留目标区域的边界特征点防止原始点云边界收缩。选取6组高密度机载LiDAR点云为数据源，将本文方法与七种经典点云简化方法（包括随机方法、体素格网方法、基于曲率的方法、最大Z容差方法、基于图的方法、基于多指标加权方法和基于迭代的简化方法）比较分析。实验结果表明：与其他传统方法相比，本文方法生成的数字高程模型（DEM）的平均RMSE至少降低了12.1 %，平均MAE至少降低了9.6 %，其派生品（包括平均坡度和地形粗糙度）与参考值也更为接近，而且较好的保留了地形特征信息。

Abstract

Objective Point cloud simplification is a prerequisite for efficient transmission and multi-scale applications of massive airborne LiDAR ground point clouds. However

existing ground point cloud simplification methods suffer from poor applicability in complex environments and loss of terrain detail features. Method This paper proposes an Airborne LiDAR Point Cloud Clustering Simplification Algorithm Considering Terrain Features and Boundary Protection against Contraction. Firstly

the point cloud is segmented into initial point cloud clusters using the K-means algorithm. Then

based on the terrain complexity of each cluster

further subdivisions are performed. Subsequently

using the point cloud normal vector information within the subdivided sub-clusters and the elevation differences of edge points between adjacent clusters

terrain feature points at different terrains are identified. Finally

boundary feature points of the target area are preserved to prevent the contraction of the original point cloud boundary. Result In six groups of point cloud scenes with high terrain complexity

the proposed method is compared and analyzed with seven classical point cloud simplification methods

including random methods

voxel grid methods

curvature-based methods

maximum Z tolerance methods

graph-based methods

multi-index weighted methods

and iterative simplification methods. The experimental results demonstrate that

compared to other traditional methods

the proposed method achieves a minimum reduction of 12.1% in the average root mean square error (RMSE) of the generated digital elevation models (DEMs) and a minimum reduction of 9.6% in the average mean absolute error (MAE). The derived products

including average slope and terrain roughness

also exhibit closer agreement with the reference values. Qualitative analysis results indicate that the DEM constructed by the proposed method aligns better with the reference DEM and provides more accurate and detailed terrain features. Conclusion The above experimental results demonstrate that the proposed method effectively reduces the accuracy loss caused by simplification of DEMs while maintaining strong adaptability to terrain. This method can be applied to intelligent simplification of airborne LiDAR point cloud data

enabling the construction of high-precision DEMs to meet the requirements of geoscientific analysis for high accuracy and efficiency.

关键词

机载LiDAR点云简化K-means地形特征DEM精度

Keywords

airborne LiDARpoint cloud simplificationK-meansterrain featuresDEMaccuracy

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