Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas
Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observ...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/852 |
| _version_ | 1797437248044531712 |
|---|---|
| author | Lei Pang Dayuan Liu Conghua Li Fengli Zhang |
| author_facet | Lei Pang Dayuan Liu Conghua Li Fengli Zhang |
| author_sort | Lei Pang |
| collection | DOAJ |
| description | Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m. |
| first_indexed | 2024-03-09T11:17:28Z |
| format | Article |
| id | doaj.art-9b452be5137548278fe7b6a09540ae2f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:28Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9b452be5137548278fe7b6a09540ae2f2023-12-01T00:28:43ZengMDPI AGSensors1424-82202023-01-0123285210.3390/s23020852Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban AreasLei Pang0Dayuan Liu1Conghua Li2Fengli Zhang3School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaBuilding reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m.https://www.mdpi.com/1424-8220/23/2/852homologous TomoSAR point cloudcross-source TomoSAR point cloudthe normal vector of the opposite facadethe facade projection |
| spellingShingle | Lei Pang Dayuan Liu Conghua Li Fengli Zhang Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas Sensors homologous TomoSAR point cloud cross-source TomoSAR point cloud the normal vector of the opposite facade the facade projection |
| title | Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas |
| title_full | Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas |
| title_fullStr | Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas |
| title_full_unstemmed | Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas |
| title_short | Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas |
| title_sort | automatic registration of homogeneous and cross source tomosar point clouds in urban areas |
| topic | homologous TomoSAR point cloud cross-source TomoSAR point cloud the normal vector of the opposite facade the facade projection |
| url | https://www.mdpi.com/1424-8220/23/2/852 |
| work_keys_str_mv | AT leipang automaticregistrationofhomogeneousandcrosssourcetomosarpointcloudsinurbanareas AT dayuanliu automaticregistrationofhomogeneousandcrosssourcetomosarpointcloudsinurbanareas AT conghuali automaticregistrationofhomogeneousandcrosssourcetomosarpointcloudsinurbanareas AT fenglizhang automaticregistrationofhomogeneousandcrosssourcetomosarpointcloudsinurbanareas |