Version 1
: Received: 28 April 2023 / Approved: 4 May 2023 / Online: 4 May 2023 (04:36:07 CEST)
Version 2
: Received: 28 May 2023 / Approved: 30 May 2023 / Online: 30 May 2023 (05:44:25 CEST)
Kang, C.; Lin, Z.; Wu, S.; Lan, Y.; Geng, C.; Zhang, S. A Triangular Grid Filter Method Based on the Slope Filter. Remote Sens.2023, 15, 2930.
Kang, C.; Lin, Z.; Wu, S.; Lan, Y.; Geng, C.; Zhang, S. A Triangular Grid Filter Method Based on the Slope Filter. Remote Sens. 2023, 15, 2930.
Kang, C.; Lin, Z.; Wu, S.; Lan, Y.; Geng, C.; Zhang, S. A Triangular Grid Filter Method Based on the Slope Filter. Remote Sens.2023, 15, 2930.
Kang, C.; Lin, Z.; Wu, S.; Lan, Y.; Geng, C.; Zhang, S. A Triangular Grid Filter Method Based on the Slope Filter. Remote Sens. 2023, 15, 2930.
Abstract
High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.
Keywords
Slope Filter; point cloud; triangular grid; KD-Tree-Based Euclidean Clustering
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: 梓涛 林
Commenter's Conflict of Interests: Author