Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data
The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the w...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000754 |
| _version_ | 1827963112520679424 |
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| author | Guoqing Zhou Gongbei Wu Xiang Zhou Chao Xu Dawei Zhao Jinchun Lin Zhexian Liu Haotian Zhang Qingyang Wang Jiasheng Xu Bo Song Lieping Zhang |
| author_facet | Guoqing Zhou Gongbei Wu Xiang Zhou Chao Xu Dawei Zhao Jinchun Lin Zhexian Liu Haotian Zhang Qingyang Wang Jiasheng Xu Bo Song Lieping Zhang |
| author_sort | Guoqing Zhou |
| collection | DOAJ |
| description | The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do. |
| first_indexed | 2024-04-09T16:54:24Z |
| format | Article |
| id | doaj.art-0cc10b52118647d08eb0b25a025af5ab |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T16:54:24Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-0cc10b52118647d08eb0b25a025af5ab2023-04-21T06:41:12ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-04-01118103253Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud dataGuoqing Zhou0Gongbei Wu1Xiang Zhou2Chao Xu3Dawei Zhao4Jinchun Lin5Zhexian Liu6Haotian Zhang7Qingyang Wang8Jiasheng Xu9Bo Song10Lieping Zhang11College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, China; Corresponding author at: College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China.College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, China; College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian ‘Gan Road, Guilin, Guangxi 541004, ChinaCollege of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, ChinaThe water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do.http://www.sciencedirect.com/science/article/pii/S1569843223000754LiDARAdaptive modelCloud dataBathymetryWater depth biasCorrection |
| spellingShingle | Guoqing Zhou Gongbei Wu Xiang Zhou Chao Xu Dawei Zhao Jinchun Lin Zhexian Liu Haotian Zhang Qingyang Wang Jiasheng Xu Bo Song Lieping Zhang Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data International Journal of Applied Earth Observations and Geoinformation LiDAR Adaptive model Cloud data Bathymetry Water depth bias Correction |
| title | Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data |
| title_full | Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data |
| title_fullStr | Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data |
| title_full_unstemmed | Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data |
| title_short | Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data |
| title_sort | adaptive model for the water depth bias correction of bathymetric lidar point cloud data |
| topic | LiDAR Adaptive model Cloud data Bathymetry Water depth bias Correction |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000754 |
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