摘要
Abstract
Airborne light detection and ranging(LiDAR)point cloud filtering is a crucial prerequisite for generating high-precision digital elevation model(DEM).Traditional single progressive morphological filtering has certain limitations in airborne point cloud filtering,as it fails to completely remove some near-ground points that are close to the terrain surface.To address this,this paper proposed an improved cloth simulation filtering algorithm based on the progressive morphological filtering algorithm to accurately identify and eliminate near-ground points that affect DEM accuracy.The implementation process of the improved cloth simulation filtering algorithm is as follows:first,processing the point cloud data using grid-based technology based on the progressive morphological filtering results to simulate and fit the terrain surface,while preserving key terrain features;second,making precise adjustments to the relevant parameters based on the segmentation results of the point cloud data to achieve the optimal filtering effect;finally,using the deviation normalization value between the true elevation and the fitted elevation,a reasonable distance threshold is determined to enhance the accuracy of the filtering results.To validate the practical effectiveness of the filtering method proposed in this paper,airborne LiDAR point cloud data from a region in Pu'an County,Guizhou Province,was selected for experimentation.The experimental results show that the proposed filtering method significantly improves the accuracy of point cloud filtering compared to the single filtering method.Specifically,Class I errors,Class II errors,and total errors were reduced by 2.92%,1.57%,and 3.47%,respectively,while the Kappa coefficient increased by 0.034 2.The method demonstrated high stability and has positive potential for broader application.关键词
机载点云滤波/渐进式形态学滤波/改进布料模拟滤波/组合滤波算法/自适应阈值Key words
airborne point cloud filtering/progressive morphological filtering/improved cloth simulation filtering/combined filtering algorithm/adaptive threshold分类
测绘与仪器
