A slope-constraint PTD filtering algorithm for LiDAR point clouds

Xiaotian Shi; Dong Wang

doi:10.1117/12.2667425

10 February 2023 A slope-constraint PTD filtering algorithm for LiDAR point clouds

Xiaotian Shi, Dong Wang

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522Y (2023) https://doi.org/10.1117/12.2667425
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Filtering is one of the most important processes before applications such as Digital Elevation Model (DEM) generation, building reconstruction and tree extraction. Progressive-TIN-Densification (PTD) filtering has been proved to be effective in different types of terrain; however, PTD filtering highly relies on the predefined parameters, i.e. iterative angle and iterative distance. To improve the performance and adaptivity of PTD filtering, in this paper, we present a slope-constraint PTD (SCPTD) algorithm. The main contribution of proposed algorithm is a strategy to remove landcover objects with loose preset parameters. To test our method, seven sets of point cloud from ISPRS Working Group III/3 are utilized to test the validity of the proposed method. And fifteen samples with manual classification are used to analyze the proposed method quantitatively. Experimental results suggest that our method is effective; compared to classic PTD filtering, the total error have reduced in fourteen samples.

Citation Download Citation

Xiaotian Shi and Dong Wang "A slope-constraint PTD filtering algorithm for LiDAR point clouds", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522Y (10 February 2023); https://doi.org/10.1117/12.2667425

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