An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data
Airborne lidar is a technology for mapping surface spatial information and has been widely used in many areas of geospatial information disciplines. The filtering process of removing non-ground points has always been the focus of research. PTD (Progressive Triangular Irregular Network Densification)...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1015153/full |
| _version_ | 1797960160959791104 |
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| author | Na Chen Na Chen Na Chen Nanmeng Wang Yi He Xiang Ding Jian Kong |
| author_facet | Na Chen Na Chen Na Chen Nanmeng Wang Yi He Xiang Ding Jian Kong |
| author_sort | Na Chen |
| collection | DOAJ |
| description | Airborne lidar is a technology for mapping surface spatial information and has been widely used in many areas of geospatial information disciplines. The filtering process of removing non-ground points has always been the focus of research. PTD (Progressive Triangular Irregular Network Densification) filtering algorithm is a widely used filtering algorithm for airborne lidar data. However, this algorithm has shortcomings in retaining ground points in steep areas, leading to large type Ⅰ errors. Therefore, this paper proposes an improved PTD algorithm. The improvement is the addition of the seed points filtering. Specifically, after the potential seed points are obtained by the progressive morphological filter, the seed points filtering is performed on it to remove the non-ground points, so that the obtained seed points are more accurate. The benchmark dataset of ISPRS (International Society for Photogrammetry and Remote Sensing) Working Group III is used to assess the proposed method. Results show that the method is effective in decreasing type Ⅰ error in steep areas. Comparing with the classic PTD algorithm, the type Ⅰ error and total error are decreased by 8.46% and 5.06% respectively. In addition, the proposed method shows a great advantage in computational efficiency, that is eight times more efficient than the classic PTD algorithm. |
| first_indexed | 2024-04-11T00:41:57Z |
| format | Article |
| id | doaj.art-ea8e8c85fc8149cc9f1b423b9f5ec1f8 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-11T00:41:57Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-ea8e8c85fc8149cc9f1b423b9f5ec1f82023-01-06T05:03:55ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10151531015153An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR dataNa Chen0Na Chen1Na Chen2Nanmeng Wang3Yi He4Xiang Ding5Jian Kong6School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, ChinaHubei Key Laboratory of Blasting Engineering of Jianghan University, Wuhan, ChinaDepartment of Mining and Geological Engineering, University of Arizona, Tucson, AZ, United StatesSchool of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, ChinaHubei University of Technology Engineering and Technology College, Wuhan, ChinaSchool of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, ChinaChangjiang Engineering Group, Wuhan, ChinaAirborne lidar is a technology for mapping surface spatial information and has been widely used in many areas of geospatial information disciplines. The filtering process of removing non-ground points has always been the focus of research. PTD (Progressive Triangular Irregular Network Densification) filtering algorithm is a widely used filtering algorithm for airborne lidar data. However, this algorithm has shortcomings in retaining ground points in steep areas, leading to large type Ⅰ errors. Therefore, this paper proposes an improved PTD algorithm. The improvement is the addition of the seed points filtering. Specifically, after the potential seed points are obtained by the progressive morphological filter, the seed points filtering is performed on it to remove the non-ground points, so that the obtained seed points are more accurate. The benchmark dataset of ISPRS (International Society for Photogrammetry and Remote Sensing) Working Group III is used to assess the proposed method. Results show that the method is effective in decreasing type Ⅰ error in steep areas. Comparing with the classic PTD algorithm, the type Ⅰ error and total error are decreased by 8.46% and 5.06% respectively. In addition, the proposed method shows a great advantage in computational efficiency, that is eight times more efficient than the classic PTD algorithm.https://www.frontiersin.org/articles/10.3389/feart.2022.1015153/fullairborne lidarPTDprogressive morphological filterseed points filteringfiltering algorithm |
| spellingShingle | Na Chen Na Chen Na Chen Nanmeng Wang Yi He Xiang Ding Jian Kong An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data Frontiers in Earth Science airborne lidar PTD progressive morphological filter seed points filtering filtering algorithm |
| title | An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data |
| title_full | An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data |
| title_fullStr | An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data |
| title_full_unstemmed | An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data |
| title_short | An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data |
| title_sort | improved progressive triangular irregular network densification filtering algorithm for airborne lidar data |
| topic | airborne lidar PTD progressive morphological filter seed points filtering filtering algorithm |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1015153/full |
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