Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas
Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classificat...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/21/4685 |
| _version_ | 1811301633592655872 |
|---|---|
| author | Yetao Yang Ke Wu Yi Wang Tao Chen Xiang Wang |
| author_facet | Yetao Yang Ke Wu Yi Wang Tao Chen Xiang Wang |
| author_sort | Yetao Yang |
| collection | DOAJ |
| description | Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency. |
| first_indexed | 2024-04-13T07:12:29Z |
| format | Article |
| id | doaj.art-6251030e9f154a269cf9a945adcc2bdb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:12:29Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6251030e9f154a269cf9a945adcc2bdb2022-12-22T02:56:50ZengMDPI AGSensors1424-82202019-10-011921468510.3390/s19214685s19214685Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban AreasYetao Yang0Ke Wu1Yi Wang2Tao Chen3Xiang Wang4Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Bridge Structure Health and Safety, Wuhan 430034, ChinaClassifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency.https://www.mdpi.com/1424-8220/19/21/4685lidar point cloudclassificationgraph cutshierarchical graph |
| spellingShingle | Yetao Yang Ke Wu Yi Wang Tao Chen Xiang Wang Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas Sensors lidar point cloud classification graph cuts hierarchical graph |
| title | Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas |
| title_full | Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas |
| title_fullStr | Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas |
| title_full_unstemmed | Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas |
| title_short | Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas |
| title_sort | two layered graph cuts based classification of lidar data in urban areas |
| topic | lidar point cloud classification graph cuts hierarchical graph |
| url | https://www.mdpi.com/1424-8220/19/21/4685 |
| work_keys_str_mv | AT yetaoyang twolayeredgraphcutsbasedclassificationoflidardatainurbanareas AT kewu twolayeredgraphcutsbasedclassificationoflidardatainurbanareas AT yiwang twolayeredgraphcutsbasedclassificationoflidardatainurbanareas AT taochen twolayeredgraphcutsbasedclassificationoflidardatainurbanareas AT xiangwang twolayeredgraphcutsbasedclassificationoflidardatainurbanareas |