A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas
It is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in preparation for satellite LiDAR point clouds in roug...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/13/2163 |
| _version_ | 1797563127891492864 |
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| author | Ruqin Zhou Wanshou Jiang |
| author_facet | Ruqin Zhou Wanshou Jiang |
| author_sort | Ruqin Zhou |
| collection | DOAJ |
| description | It is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in preparation for satellite LiDAR point clouds in rough areas. This method has several merits: (1) only ridgelines are extracted as neighbor information for feature description and their intersections are extracted as keypoints, which can greatly reduce the number of points for subsequent processing, and extracted keypoints is of high repeatability and distinctiveness; (2) a new local-reference frame (LRF) construction method is designed by combining both three dimensional (3D) coordinate and normal vector covariance matrices, which effectively improves its direction consistency; (3) a minimum cost–maximum flow (MCMF) graph-matching strategy is adopted to maximize similarity sum in a sparse-matching graph. It can avoid the problem of “many-to-many” and “one to many” caused by traditional matching strategies; (4) a transformation matrix-based clustering is adopted with a least square (LS)-based registration, where mismatches are eliminated and correct pairs are fully participated in optimal parameters evaluation to improve its stability. Experiments on simulated satellite LiDAR point clouds show that this method can effectively remove mismatches and estimate optimal parameters with high accuracy, especially in rough areas. |
| first_indexed | 2024-03-10T18:38:14Z |
| format | Article |
| id | doaj.art-1671853cdcb0486482c5a5edeb503158 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:38:14Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1671853cdcb0486482c5a5edeb5031582023-11-20T06:00:30ZengMDPI AGRemote Sensing2072-42922020-07-011213216310.3390/rs12132163A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough AreasRuqin Zhou0Wanshou Jiang1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaIt is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in preparation for satellite LiDAR point clouds in rough areas. This method has several merits: (1) only ridgelines are extracted as neighbor information for feature description and their intersections are extracted as keypoints, which can greatly reduce the number of points for subsequent processing, and extracted keypoints is of high repeatability and distinctiveness; (2) a new local-reference frame (LRF) construction method is designed by combining both three dimensional (3D) coordinate and normal vector covariance matrices, which effectively improves its direction consistency; (3) a minimum cost–maximum flow (MCMF) graph-matching strategy is adopted to maximize similarity sum in a sparse-matching graph. It can avoid the problem of “many-to-many” and “one to many” caused by traditional matching strategies; (4) a transformation matrix-based clustering is adopted with a least square (LS)-based registration, where mismatches are eliminated and correct pairs are fully participated in optimal parameters evaluation to improve its stability. Experiments on simulated satellite LiDAR point clouds show that this method can effectively remove mismatches and estimate optimal parameters with high accuracy, especially in rough areas.https://www.mdpi.com/2072-4292/12/13/2163satellite LiDAR point cloudslocal-reference framegraph-matchingterrain registration |
| spellingShingle | Ruqin Zhou Wanshou Jiang A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas Remote Sensing satellite LiDAR point clouds local-reference frame graph-matching terrain registration |
| title | A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas |
| title_full | A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas |
| title_fullStr | A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas |
| title_full_unstemmed | A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas |
| title_short | A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas |
| title_sort | ridgeline based terrain co registration for satellite lidar point clouds in rough areas |
| topic | satellite LiDAR point clouds local-reference frame graph-matching terrain registration |
| url | https://www.mdpi.com/2072-4292/12/13/2163 |
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