Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

编号 030026002

推送时间 20201012

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

来源期刊 IEEE ACCESS

期 第260期

发表时间 20200310

关键词 Filtering algorithms;  Three-dimensional displays;  Forestry;  Surface morphology;  Laser radar;  Information filtering;  Filtering;  point cloud;  interpolation;  digital elevation model; 

摘要 Over the past decades, plenty of filtering algorithms have been presented to distinguish ground and non-ground points from?airborne?LiDAR?point clouds. However, with the existing methods, it is difficult to derive satisfactory filtering results on rugged terrains with dense vegetation due to the low-level penetration ability of laser pulses. Therefore, a multi-level interpolation-based filter is developed in this paper. The novelty of the algorithm lies within its usage of multi-scale morphological operations and robust z-score to correctly select ground seeds as more as possible, and a terrain-adaptive residual threshold to adapt to various terrain characteristics. Rural samples provided by International Society for Photogrammetry and Remote Sensing (ISPRS) were employed to assess the performance of the proposed method and its results were compared with 15 filtering algorithms developed in recent 5 years (2015-2019). Results show that the proposed method with the optimized parameters produces the best accuracy with the average total error and kappa coefficient of 1.89% and 87.88%, respectively. We further filtered high-density point clouds in six forested areas with different vegetation covers and terrain slopes. Results demonstrate that the proposed algorithm is more accurate than the well-known filtering methods including morphological-based filter, progressive TIN densification filter (PTD), improved PTD and cloth simulation filter, with the average total error decreased by 26.2%, 19.9%, 3.8% and 40.4%, respectively. Moreover, the DEMs of the proposed method have lower average root mean square errors than the four classical filters. Therefore, the proposed method can be considered as an effective ground filtering algorithm for?airborne?LiDAR?point clouds in forested areas.

服务人员 付贺龙

PDF文件 浏览全文