A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application
Tomographic synthetic aperture radar (TomoSAR) has the ability to separate mixed scatterers, making it highly suitable for urban 3-dimensional (3D) reconstruction. However, Urban TomoSAR imaging still faces challenges such as resolution limitations, multipath effects, the uncertainty on the flight t...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0583 |
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| author | Chunyi Wang Qiancheng Yan Xiaolan Qiu Yitong Luo Lingxiao Peng Zhe Zhang |
| author_facet | Chunyi Wang Qiancheng Yan Xiaolan Qiu Yitong Luo Lingxiao Peng Zhe Zhang |
| author_sort | Chunyi Wang |
| collection | DOAJ |
| description | Tomographic synthetic aperture radar (TomoSAR) has the ability to separate mixed scatterers, making it highly suitable for urban 3-dimensional (3D) reconstruction. However, Urban TomoSAR imaging still faces challenges such as resolution limitations, multipath effects, the uncertainty on the flight track, and registration errors, resulting in sparse point clouds with holes and low accuracy. In this paper, we propose a Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm (Geo-SETRA) for urban area. Geo-SETRA integrates geometric structures, extracted from TomoSAR point clouds, as prior distributions for elevation estimation using Bayesian methods. We first construct a sparse optimization model based on both compressed sensing and maximum a posteriori estimation, and also give its solution. Further, the Cramér–Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy. Both simulated data and real-data experiments prove that our method is feasible and effective in urban 3D reconstruction. As a result, our method successfully produced a dense and realistic 3D scattering model for urban areas with minimal postprocessing, preserving detailed geometric structures and retaining over 80% of the points in the final model. |
| format | Article |
| id | doaj-art-acf5d722d18e47dbab7e1c0adfe04a42 |
| institution | OA Journals |
| issn | 2694-1589 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Journal of Remote Sensing |
| spelling | doaj-art-acf5d722d18e47dbab7e1c0adfe04a422025-08-20T02:21:33ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892025-01-01510.34133/remotesensing.0583A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and ApplicationChunyi Wang0Qiancheng Yan1Xiaolan Qiu2Yitong Luo3Lingxiao Peng4Zhe Zhang5Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China.National Key Laboratory of Microwave Imaging, Beijing 100190, China.Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China.Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China.Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China.Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China.Tomographic synthetic aperture radar (TomoSAR) has the ability to separate mixed scatterers, making it highly suitable for urban 3-dimensional (3D) reconstruction. However, Urban TomoSAR imaging still faces challenges such as resolution limitations, multipath effects, the uncertainty on the flight track, and registration errors, resulting in sparse point clouds with holes and low accuracy. In this paper, we propose a Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm (Geo-SETRA) for urban area. Geo-SETRA integrates geometric structures, extracted from TomoSAR point clouds, as prior distributions for elevation estimation using Bayesian methods. We first construct a sparse optimization model based on both compressed sensing and maximum a posteriori estimation, and also give its solution. Further, the Cramér–Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy. Both simulated data and real-data experiments prove that our method is feasible and effective in urban 3D reconstruction. As a result, our method successfully produced a dense and realistic 3D scattering model for urban areas with minimal postprocessing, preserving detailed geometric structures and retaining over 80% of the points in the final model.https://spj.science.org/doi/10.34133/remotesensing.0583 |
| spellingShingle | Chunyi Wang Qiancheng Yan Xiaolan Qiu Yitong Luo Lingxiao Peng Zhe Zhang A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application Journal of Remote Sensing |
| title | A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application |
| title_full | A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application |
| title_fullStr | A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application |
| title_full_unstemmed | A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application |
| title_short | A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application |
| title_sort | geometric semantic enhanced tomosar reconstruction algorithm in an urban area analysis and application |
| url | https://spj.science.org/doi/10.34133/remotesensing.0583 |
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