An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features
Addressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplification from 3D point cloud data processing and propo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2299 |
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| _version_ | 1846104176547856384 |
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| author | Dong Wang Jian Dong Lulu Tang Mengkai Ma Tian Xie |
| author_facet | Dong Wang Jian Dong Lulu Tang Mengkai Ma Tian Xie |
| author_sort | Dong Wang |
| collection | DOAJ |
| description | Addressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplification from 3D point cloud data processing and proposes a block-based DDM automatic generalization method that leverages surface curvature features. Initially, a clustering blocking model is established using an improved K-means algorithm for partitioning DDM data. Subsequently, a fitting surface is constructed based on the neighboring depth points within the blocked DDM to obtain the surface curvature characteristics of each depth point, which serve as the criterion for the DDM automatic generalization process. By integrating a multi-threaded parallel computation model, an efficient automated generalization workflow that encompasses data partitioning, fitting, computation, processing, and integration of the DDM is ultimately constructed. Furthermore, this paper designs validity and comparative experiments to analyze the proposed algorithm through experimental analysis. The results demonstrate that the algorithm can be applied to the multi-scale construction of DDM seabed terrain, while maintaining the integrity of both flat and complex seabed landforms, and significantly enhancing the computational efficiency of the DDM automatic generalization process. |
| format | Article |
| id | doaj-art-d7a5f8e5d849444ca9c0f00c3388a09d |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-d7a5f8e5d849444ca9c0f00c3388a09d2024-12-27T14:33:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212229910.3390/jmse12122299An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature FeaturesDong Wang0Jian Dong1Lulu Tang2Mengkai Ma3Tian Xie4Department of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaAddressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplification from 3D point cloud data processing and proposes a block-based DDM automatic generalization method that leverages surface curvature features. Initially, a clustering blocking model is established using an improved K-means algorithm for partitioning DDM data. Subsequently, a fitting surface is constructed based on the neighboring depth points within the blocked DDM to obtain the surface curvature characteristics of each depth point, which serve as the criterion for the DDM automatic generalization process. By integrating a multi-threaded parallel computation model, an efficient automated generalization workflow that encompasses data partitioning, fitting, computation, processing, and integration of the DDM is ultimately constructed. Furthermore, this paper designs validity and comparative experiments to analyze the proposed algorithm through experimental analysis. The results demonstrate that the algorithm can be applied to the multi-scale construction of DDM seabed terrain, while maintaining the integrity of both flat and complex seabed landforms, and significantly enhancing the computational efficiency of the DDM automatic generalization process.https://www.mdpi.com/2077-1312/12/12/2299DDMseabed terrain constructioncurvature simplificationK-means clusteringterrain featuresparallel processing |
| spellingShingle | Dong Wang Jian Dong Lulu Tang Mengkai Ma Tian Xie An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features Journal of Marine Science and Engineering DDM seabed terrain construction curvature simplification K-means clustering terrain features parallel processing |
| title | An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features |
| title_full | An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features |
| title_fullStr | An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features |
| title_full_unstemmed | An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features |
| title_short | An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features |
| title_sort | automatic generalization method of a block based digital depth model based on surface curvature features |
| topic | DDM seabed terrain construction curvature simplification K-means clustering terrain features parallel processing |
| url | https://www.mdpi.com/2077-1312/12/12/2299 |
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