Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings
Reliable registration of aerial images and airborne LiDAR point clouds (ALS) is challenging during the integrated three-dimensional reconstruction of the two types of data. This study proposes a novel method for exploiting the global and local geometric constraints of buildings to automatically acqu...
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| Format: | Article |
| Language: | English |
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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/10948154/ |
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| author | Wen Li Min Chen Meixi Huang Han Hu Tong Fang Xuming Ge Qing Zhu Bo Xu Gui Gao |
| author_facet | Wen Li Min Chen Meixi Huang Han Hu Tong Fang Xuming Ge Qing Zhu Bo Xu Gui Gao |
| author_sort | Wen Li |
| collection | DOAJ |
| description | Reliable registration of aerial images and airborne LiDAR point clouds (ALS) is challenging during the integrated three-dimensional reconstruction of the two types of data. This study proposes a novel method for exploiting the global and local geometric constraints of buildings to automatically acquire reliable tie points between aerial images and ALS. First, dense matched point clouds (MPS) are obtained from aerial images. Building instances are extracted from the MPS and ALS using simple filtering and clustering algorithms, respectively. Owing to the similarity of the global geometric distribution of buildings in MPS and ALS, the relationships of building instances are established using a graph-matching method. Furthermore, to coarsely align MPS and ALS, line segments are extracted and matched with the local constraints of corresponding building instances. The proposed strategy can overcome the adverse effects of the significant differences between MPS and ALS in terms of density, completeness, and accuracy of the matching results. Moreover, the constraints of corresponding buildings greatly narrow the search range of line segment matching, improving the matching reliability and efficiency. Considering the local geometric deformations in MPS, the iterative closest point algorithm is used for each pair of buildings to realize precise registration. Subsequently, a strategy to select reliable tie points between aerial images and ALS is proposed for improving the accuracy of the orientation parameters of aerial images. The proposed method realizes the accurate registration of aerial images and ALS in two different scenes. The average projection errors of aerial images and ALS on two datasets are as low as 1.16 and 0.89 pixels, respectively. |
| format | Article |
| id | doaj-art-e8962a9c4c2049deb0b58c2fe6f53eab |
| institution | OA Journals |
| 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-e8962a9c4c2049deb0b58c2fe6f53eab2025-08-20T01:48:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118110931111010.1109/JSTARS.2025.355728210948154Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of BuildingsWen Li0https://orcid.org/0009-0007-3550-2982Min Chen1https://orcid.org/0000-0003-1381-7290Meixi Huang2Han Hu3https://orcid.org/0000-0003-1137-2208Tong Fang4https://orcid.org/0009-0007-6915-5907Xuming Ge5https://orcid.org/0000-0002-1032-1938Qing Zhu6Bo Xu7https://orcid.org/0000-0001-6049-8005Gui Gao8https://orcid.org/0000-0003-4596-5829Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, ChinaReliable registration of aerial images and airborne LiDAR point clouds (ALS) is challenging during the integrated three-dimensional reconstruction of the two types of data. This study proposes a novel method for exploiting the global and local geometric constraints of buildings to automatically acquire reliable tie points between aerial images and ALS. First, dense matched point clouds (MPS) are obtained from aerial images. Building instances are extracted from the MPS and ALS using simple filtering and clustering algorithms, respectively. Owing to the similarity of the global geometric distribution of buildings in MPS and ALS, the relationships of building instances are established using a graph-matching method. Furthermore, to coarsely align MPS and ALS, line segments are extracted and matched with the local constraints of corresponding building instances. The proposed strategy can overcome the adverse effects of the significant differences between MPS and ALS in terms of density, completeness, and accuracy of the matching results. Moreover, the constraints of corresponding buildings greatly narrow the search range of line segment matching, improving the matching reliability and efficiency. Considering the local geometric deformations in MPS, the iterative closest point algorithm is used for each pair of buildings to realize precise registration. Subsequently, a strategy to select reliable tie points between aerial images and ALS is proposed for improving the accuracy of the orientation parameters of aerial images. The proposed method realizes the accurate registration of aerial images and ALS in two different scenes. The average projection errors of aerial images and ALS on two datasets are as low as 1.16 and 0.89 pixels, respectively.https://ieeexplore.ieee.org/document/10948154/Aerial imageglobal geometric distributionLiDAR point cloudslocal line segmentsregistration |
| spellingShingle | Wen Li Min Chen Meixi Huang Han Hu Tong Fang Xuming Ge Qing Zhu Bo Xu Gui Gao Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Aerial image global geometric distribution LiDAR point clouds local line segments registration |
| title | Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings |
| title_full | Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings |
| title_fullStr | Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings |
| title_full_unstemmed | Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings |
| title_short | Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings |
| title_sort | registration of aerial images and lidar point clouds by exploiting global x2013 local geometric constraints of buildings |
| topic | Aerial image global geometric distribution LiDAR point clouds local line segments registration |
| url | https://ieeexplore.ieee.org/document/10948154/ |
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