Removal of LiDAR Negative Outliers Based on Retroreflective Surface
LiDAR is an essential tool for terrain data acquisition; however, its application in coastal environments is often limited by negative outliers caused by multipath reflections. The negative outliers can result in deviations of several meters, significantly complicating subsequent data processing and...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11066278/ |
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| author | Haolong Gao Shaobo Li Zhouyi Kang Fan Zhang Yi Zhang Yunlong Wu |
| author_facet | Haolong Gao Shaobo Li Zhouyi Kang Fan Zhang Yi Zhang Yunlong Wu |
| author_sort | Haolong Gao |
| collection | DOAJ |
| description | LiDAR is an essential tool for terrain data acquisition; however, its application in coastal environments is often limited by negative outliers caused by multipath reflections. The negative outliers can result in deviations of several meters, significantly complicating subsequent data processing and analysis. This article investigates the retroreflective characteristics of negative outliers in terms of spatial structure and intensity and presents a negative outlier removal algorithm based on these features. First, the LiDAR surveying equation is introduced to establish the intensity relationship between negative outliers and their corresponding preliminary reflection points. Second, by analyzing the spatial distribution of point clouds, a covariance matrix is generated, and eigenvalue decomposition is performed to extract structural descriptors for identifying outliers. Third, a terrain mesh model is constructed to approximate the retroreflective surface, enabling a feature-based comparison between negative outliers and their preliminary reflection points. Finally, points below the terrain mesh and their corresponding reflection points are extracted. By comparing their structural similarity and intensity relationships, negative outliers are accurately identified and removed. Experimental results validate the effectiveness of the proposed algorithm, achieving a precision of 88.97% and a recall of 91.94%, ensuring robust outlier removal while preserving terrain details. |
| format | Article |
| id | doaj-art-13a364b5aeaa433aa5c4c8e911b1f0bb |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-13a364b5aeaa433aa5c4c8e911b1f0bb2025-08-20T03:13:26ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118168311684310.1109/JSTARS.2025.358553411066278Removal of LiDAR Negative Outliers Based on Retroreflective SurfaceHaolong Gao0https://orcid.org/0009-0009-1485-1504Shaobo Li1https://orcid.org/0000-0002-1208-7778Zhouyi Kang2Fan Zhang3Yi Zhang4https://orcid.org/0000-0003-1006-6372Yunlong Wu5https://orcid.org/0000-0002-5487-5078Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaHubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaAnhui Agricultural University, Academy of Forestry, Hefei, ChinaWuhan Railway Bridge Vocational College, Wuhan, ChinaKey Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan, ChinaHubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaLiDAR is an essential tool for terrain data acquisition; however, its application in coastal environments is often limited by negative outliers caused by multipath reflections. The negative outliers can result in deviations of several meters, significantly complicating subsequent data processing and analysis. This article investigates the retroreflective characteristics of negative outliers in terms of spatial structure and intensity and presents a negative outlier removal algorithm based on these features. First, the LiDAR surveying equation is introduced to establish the intensity relationship between negative outliers and their corresponding preliminary reflection points. Second, by analyzing the spatial distribution of point clouds, a covariance matrix is generated, and eigenvalue decomposition is performed to extract structural descriptors for identifying outliers. Third, a terrain mesh model is constructed to approximate the retroreflective surface, enabling a feature-based comparison between negative outliers and their preliminary reflection points. Finally, points below the terrain mesh and their corresponding reflection points are extracted. By comparing their structural similarity and intensity relationships, negative outliers are accurately identified and removed. Experimental results validate the effectiveness of the proposed algorithm, achieving a precision of 88.97% and a recall of 91.94%, ensuring robust outlier removal while preserving terrain details.https://ieeexplore.ieee.org/document/11066278/LiDARmultipath reflectionsnegative outliersretroreflective surface |
| spellingShingle | Haolong Gao Shaobo Li Zhouyi Kang Fan Zhang Yi Zhang Yunlong Wu Removal of LiDAR Negative Outliers Based on Retroreflective Surface IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing LiDAR multipath reflections negative outliers retroreflective surface |
| title | Removal of LiDAR Negative Outliers Based on Retroreflective Surface |
| title_full | Removal of LiDAR Negative Outliers Based on Retroreflective Surface |
| title_fullStr | Removal of LiDAR Negative Outliers Based on Retroreflective Surface |
| title_full_unstemmed | Removal of LiDAR Negative Outliers Based on Retroreflective Surface |
| title_short | Removal of LiDAR Negative Outliers Based on Retroreflective Surface |
| title_sort | removal of lidar negative outliers based on retroreflective surface |
| topic | LiDAR multipath reflections negative outliers retroreflective surface |
| url | https://ieeexplore.ieee.org/document/11066278/ |
| work_keys_str_mv | AT haolonggao removaloflidarnegativeoutliersbasedonretroreflectivesurface AT shaoboli removaloflidarnegativeoutliersbasedonretroreflectivesurface AT zhouyikang removaloflidarnegativeoutliersbasedonretroreflectivesurface AT fanzhang removaloflidarnegativeoutliersbasedonretroreflectivesurface AT yizhang removaloflidarnegativeoutliersbasedonretroreflectivesurface AT yunlongwu removaloflidarnegativeoutliersbasedonretroreflectivesurface |