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

作　　者：武慧明 陈传法[1,2] 孙延宁 郭娇娇[1,2] 贝祎轩 WU Huiming;CHEN Chuanfa;SUN Yanning;GUO Jiaojiao;BEI Yixuan(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;Key Laboratory of Geomatics and Digital Technology of Shandong Province,Shandong University of Science and Technology,Qingdao 266590,China;Water Resources Research Institute of Shandong Province,Ji’nan 250101,China)

机构地区：[1]山东科技大学测绘与空间信息学院,青岛266590 [2]山东省基础地理信息与数字化技术重点实验室,青岛266590 [3]山东省水利科学研究院,济南250101

出　　处：《遥感学报》2025年第1期314-328,共15页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42271438);山东省自然科学基金(编号:ZR2024MD040)。

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。此外,选取6组高密度机载LiDAR点云为数据源,将本文方法与7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)进行比较分析。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。Point cloud simplification is a prerequisite for efficient transmission and multiscale 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.This study proposes an airborne LiDAR point cloud clustering simplification algorithm considering terrain features and boundary protection against contraction.First,the point cloud is segmented into initial point cloud clusters using k-means algorithm.Then,further subdivisions are performed on the basis of the terrain complexity of each cluster.Subsequently,terrain feature points at different terrains are identified using the point cloud normal vector information within the subdivided subclusters and the elevation differences of edge points between adjacent clusters.Finally,boundary feature points of the target area are preserved to prevent the contraction of the original point cloud boundary.In six groups of point cloud scenes with high terrain complexity,the proposed method is analyzed and compared with seven classical point cloud simplification methods,namely,random,voxel grid,curvature-based,maximum Z tolerance,graph-based,multi-index weighted,and iterative simplification methods.The experimental results demonstrate that compared with the traditional methods,the proposed method achieves a minimum reduction of 12.1%in the average root-mean-square error of the generated digital elevation models(DEMs)and a minimum reduction of 9.6%in the average mean absolute error.The derived products,including average slope and terrain roughness,also exhibit closer agreement with the reference values.The 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.The above experimental results demonstrate that the proposed method effectively reduces the accuracy loss caused by simplification of DEMs while maintaining s

关 键 词：遥感 机载LIDAR 点云简化 K-MEANS 地形特征 数字高程模型 

分 类 号：P23[天文地球—摄影测量与遥感] P2[天文地球—测绘科学与技术]