Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis
Accurate water-land classification in coastal zones is the basis of airborne LiDAR bathymetry (ALB)-based hydrological research and topographical map production. Considering the confusion among waveform features and the diverse geometric features of targets, it is difficult to distinguish water and...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-04-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000900 |
| _version_ | 1827963128006049792 |
|---|---|
| author | Yadong Guo Chengkai Feng Wenxue Xu Yanxiong Liu Dianpeng Su Chao Qi Zhipeng Dong |
| author_facet | Yadong Guo Chengkai Feng Wenxue Xu Yanxiong Liu Dianpeng Su Chao Qi Zhipeng Dong |
| author_sort | Yadong Guo |
| collection | DOAJ |
| description | Accurate water-land classification in coastal zones is the basis of airborne LiDAR bathymetry (ALB)-based hydrological research and topographical map production. Considering the confusion among waveform features and the diverse geometric features of targets, it is difficult to distinguish water and land with single-wavelength ALB systems. To address these issues, a water-land classification method based on waveform feature statistics and neighborhood analysis is proposed in this paper. First, the elevations of the bimodal waveform point clouds and the thresholds calculated based on waveform feature histograms are utilized to extract coarse- and fine-scale sea surface points, respectively. Then, the thresholds, elevation variance, and geometric features in the connected region are determined to discriminate inland water points. Finally, to improve the classification accuracy, neighborhood analysis for point cloud rasterization is performed. The proposed method is verified with four swaths obtained by the Optech Aquarius ALB system near Wuzhizhou Island and Yuanzhi Island in the South China Sea. Overall accuracy values of 99.2% and 95.2% on average are obtained using the proposed method for all points and the 100 m coastline buffer, respectively. In comparison, a higher precision and shorter runtime are achieved using the proposed method than the support vector machine (SVM), random forest (RF), and fuzzy C-means (FCM) methods. Accordingly, the proposed method is a precise and efficient water-land classification method for single-wavelength ALB systems without artificial samples. In the future, this method can provide an effective technical approach for the fully automatic processing of ALB data. |
| first_indexed | 2024-04-09T16:54:39Z |
| format | Article |
| id | doaj.art-45d622e0c04f40fdabe64ffa58ebaf8b |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T16:54:39Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-45d622e0c04f40fdabe64ffa58ebaf8b2023-04-21T06:41:20ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-04-01118103268Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysisYadong Guo0Chengkai Feng1Wenxue Xu2Yanxiong Liu3Dianpeng Su4Chao Qi5Zhipeng Dong6College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China; Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China; Corresponding author at: First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China; Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaAccurate water-land classification in coastal zones is the basis of airborne LiDAR bathymetry (ALB)-based hydrological research and topographical map production. Considering the confusion among waveform features and the diverse geometric features of targets, it is difficult to distinguish water and land with single-wavelength ALB systems. To address these issues, a water-land classification method based on waveform feature statistics and neighborhood analysis is proposed in this paper. First, the elevations of the bimodal waveform point clouds and the thresholds calculated based on waveform feature histograms are utilized to extract coarse- and fine-scale sea surface points, respectively. Then, the thresholds, elevation variance, and geometric features in the connected region are determined to discriminate inland water points. Finally, to improve the classification accuracy, neighborhood analysis for point cloud rasterization is performed. The proposed method is verified with four swaths obtained by the Optech Aquarius ALB system near Wuzhizhou Island and Yuanzhi Island in the South China Sea. Overall accuracy values of 99.2% and 95.2% on average are obtained using the proposed method for all points and the 100 m coastline buffer, respectively. In comparison, a higher precision and shorter runtime are achieved using the proposed method than the support vector machine (SVM), random forest (RF), and fuzzy C-means (FCM) methods. Accordingly, the proposed method is a precise and efficient water-land classification method for single-wavelength ALB systems without artificial samples. In the future, this method can provide an effective technical approach for the fully automatic processing of ALB data.http://www.sciencedirect.com/science/article/pii/S1569843223000900Water-land classificationAirborne LiDAR bathymetry (ALB)Waveform feature statisticsNeighborhood analysisSea surface elevation |
| spellingShingle | Yadong Guo Chengkai Feng Wenxue Xu Yanxiong Liu Dianpeng Su Chao Qi Zhipeng Dong Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis International Journal of Applied Earth Observations and Geoinformation Water-land classification Airborne LiDAR bathymetry (ALB) Waveform feature statistics Neighborhood analysis Sea surface elevation |
| title | Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| title_full | Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| title_fullStr | Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| title_full_unstemmed | Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| title_short | Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| title_sort | water land classification for single wavelength airborne lidar bathymetry based on waveform feature statistics and point cloud neighborhood analysis |
| topic | Water-land classification Airborne LiDAR bathymetry (ALB) Waveform feature statistics Neighborhood analysis Sea surface elevation |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000900 |
| work_keys_str_mv | AT yadongguo waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT chengkaifeng waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT wenxuexu waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT yanxiongliu waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT dianpengsu waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT chaoqi waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis AT zhipengdong waterlandclassificationforsinglewavelengthairbornelidarbathymetrybasedonwaveformfeaturestatisticsandpointcloudneighborhoodanalysis |