Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models
The photogrammetric 3-D textured mesh model (TMM) obtained by unmanned aerial vehicle provides accurate geometric shapes and realistic textures. The 3-D semantics derived from TMMs serve as the foundation in many applications, such as urban planning, forestry, and smart city. Although 3-D TMM provid...
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| Format: | Article |
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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/10756635/ |
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| author | Zhongwen Hu Jinhua Zhang Zhigang Liu Yinghui Zhang Jingzhe Wang Qian Zhang Guofeng Wu |
| author_facet | Zhongwen Hu Jinhua Zhang Zhigang Liu Yinghui Zhang Jingzhe Wang Qian Zhang Guofeng Wu |
| author_sort | Zhongwen Hu |
| collection | DOAJ |
| description | The photogrammetric 3-D textured mesh model (TMM) obtained by unmanned aerial vehicle provides accurate geometric shapes and realistic textures. The 3-D semantics derived from TMMs serve as the foundation in many applications, such as urban planning, forestry, and smart city. Although 3-D TMM provides more features than 2-D images, current classification methods have not fully utilized these features, particularly the stereoscopic hierarchical structure of different objects. To address this issue, we propose a hierarchical object-based method for the classification of TMMs, consisting of three key steps: 1) the TMM is first hierarchically segmented into ground surface meshes and off-ground 3-D objects using a cloth-simulated filtering algorithm; 2) the ground surface mesh is projected to 2-D ortho-image, where object-based image classification is used to classify pixels. The resulting semantic labels of pixels are then mapped back to the corresponding mesh model; 3) instance-level 3-D objects are created through connected component analysis of off-ground meshes, and then classified via a 3-D object-based approach. The 3-D semantic model is generated by merging the outcomes of steps 2) and 3). Experimental results indicate that the stereoscopic hierarchical strategy effectively decomposes a 3-D scene into simple ground surfaces and off-ground 3-D objects, yielding improvements in both accuracy and efficiency. Our proposed method demonstrates an accuracy increase of over 7%–36% compared to existing methods. This is the first time a stereoscopic hierarchy has been introduced for classifying 3-D textured mesh model, providing a valuable reference for future classification methods. |
| format | Article |
| id | doaj-art-fbacee1e7b4f46f8ba9a202ae04801ab |
| institution | Kabale University |
| 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-fbacee1e7b4f46f8ba9a202ae04801ab2025-01-16T00:00:32ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011875776810.1109/JSTARS.2024.350001210756635Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh ModelsZhongwen Hu0https://orcid.org/0000-0003-2689-3196Jinhua Zhang1Zhigang Liu2Yinghui Zhang3https://orcid.org/0000-0001-6980-2384Jingzhe Wang4https://orcid.org/0000-0001-8332-7997Qian Zhang5Guofeng Wu6https://orcid.org/0000-0003-2275-6530MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaSchool of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaThe photogrammetric 3-D textured mesh model (TMM) obtained by unmanned aerial vehicle provides accurate geometric shapes and realistic textures. The 3-D semantics derived from TMMs serve as the foundation in many applications, such as urban planning, forestry, and smart city. Although 3-D TMM provides more features than 2-D images, current classification methods have not fully utilized these features, particularly the stereoscopic hierarchical structure of different objects. To address this issue, we propose a hierarchical object-based method for the classification of TMMs, consisting of three key steps: 1) the TMM is first hierarchically segmented into ground surface meshes and off-ground 3-D objects using a cloth-simulated filtering algorithm; 2) the ground surface mesh is projected to 2-D ortho-image, where object-based image classification is used to classify pixels. The resulting semantic labels of pixels are then mapped back to the corresponding mesh model; 3) instance-level 3-D objects are created through connected component analysis of off-ground meshes, and then classified via a 3-D object-based approach. The 3-D semantic model is generated by merging the outcomes of steps 2) and 3). Experimental results indicate that the stereoscopic hierarchical strategy effectively decomposes a 3-D scene into simple ground surfaces and off-ground 3-D objects, yielding improvements in both accuracy and efficiency. Our proposed method demonstrates an accuracy increase of over 7%–36% compared to existing methods. This is the first time a stereoscopic hierarchy has been introduced for classifying 3-D textured mesh model, providing a valuable reference for future classification methods.https://ieeexplore.ieee.org/document/10756635/Hierarchical classificationobject-based approachrandom forest (RF) algorithmstereoscopic hierarchytextured mesh model (TMM) |
| spellingShingle | Zhongwen Hu Jinhua Zhang Zhigang Liu Yinghui Zhang Jingzhe Wang Qian Zhang Guofeng Wu Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hierarchical classification object-based approach random forest (RF) algorithm stereoscopic hierarchy textured mesh model (TMM) |
| title | Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models |
| title_full | Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models |
| title_fullStr | Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models |
| title_full_unstemmed | Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models |
| title_short | Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models |
| title_sort | hierarchical 2 d 3 d object based classification of photogrammetric textured mesh models |
| topic | Hierarchical classification object-based approach random forest (RF) algorithm stereoscopic hierarchy textured mesh model (TMM) |
| url | https://ieeexplore.ieee.org/document/10756635/ |
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