Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data
The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/4/624 |
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| author | Libin Du Dawei Wan Xiangqian Meng Wenjing Li Guangxin Liang Hongyu Li |
| author_facet | Libin Du Dawei Wan Xiangqian Meng Wenjing Li Guangxin Liang Hongyu Li |
| author_sort | Libin Du |
| collection | DOAJ |
| description | The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classifying shallow marine sediments remains a challenging endeavor due to the difficulties associated with differentiation and the inherent limitations in accuracy. To achieve the accurate classification of underwater sediments, a feature selection method for underwater sediment classification is proposed in this paper and tested in a laboratory environment. The method inputs the original feature set into a classification algorithm that combines Sequential Forward Selection with Random Forests. The study demonstrates that the model achieves an overall classification accuracy of 94.1% and a Kappa coefficient of 91.11%, thereby enabling the accurate and efficient classification of underwater sediment. This approach can be employed as a supplementary technique for the precise classification of shallow marine sediments, offering valuable assistance in the examination of marine ecosystems. |
| format | Article |
| id | doaj-art-1ca657d2f1d243ed8ea651898e18a40d |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-1ca657d2f1d243ed8ea651898e18a40d2025-08-20T02:28:36ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113462410.3390/jmse13040624Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform DataLibin Du0Dawei Wan1Xiangqian Meng2Wenjing Li3Guangxin Liang4Hongyu Li5College of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaThe classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classifying shallow marine sediments remains a challenging endeavor due to the difficulties associated with differentiation and the inherent limitations in accuracy. To achieve the accurate classification of underwater sediments, a feature selection method for underwater sediment classification is proposed in this paper and tested in a laboratory environment. The method inputs the original feature set into a classification algorithm that combines Sequential Forward Selection with Random Forests. The study demonstrates that the model achieves an overall classification accuracy of 94.1% and a Kappa coefficient of 91.11%, thereby enabling the accurate and efficient classification of underwater sediment. This approach can be employed as a supplementary technique for the precise classification of shallow marine sediments, offering valuable assistance in the examination of marine ecosystems.https://www.mdpi.com/2077-1312/13/4/624underwater sediment classificationsequential forward selectionrandom forest algorithmLiDARfull-waveform data processing |
| spellingShingle | Libin Du Dawei Wan Xiangqian Meng Wenjing Li Guangxin Liang Hongyu Li Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data Journal of Marine Science and Engineering underwater sediment classification sequential forward selection random forest algorithm LiDAR full-waveform data processing |
| title | Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data |
| title_full | Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data |
| title_fullStr | Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data |
| title_full_unstemmed | Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data |
| title_short | Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data |
| title_sort | classification of underwater sediments in lab based on lidar full waveform data |
| topic | underwater sediment classification sequential forward selection random forest algorithm LiDAR full-waveform data processing |
| url | https://www.mdpi.com/2077-1312/13/4/624 |
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