5小时以上的长途客车之旅会让人感到身心疲惫.如果在汽车上连续待70天的话。对常人来说.那简直就是一种对身体和意志力的折磨。而在奥运开幕式前夕.由德国旅游运营商Avanti-Reisen组织的一次历经70天、以Setra S 415 HDH客车为交通工具...5小时以上的长途客车之旅会让人感到身心疲惫.如果在汽车上连续待70天的话。对常人来说.那简直就是一种对身体和意志力的折磨。而在奥运开幕式前夕.由德国旅游运营商Avanti-Reisen组织的一次历经70天、以Setra S 415 HDH客车为交通工具、穿越德国、意大利、中国等9个国家的探险旅行团最终顺利抵达北京。展开更多
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 a...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.展开更多
文摘5小时以上的长途客车之旅会让人感到身心疲惫.如果在汽车上连续待70天的话。对常人来说.那简直就是一种对身体和意志力的折磨。而在奥运开幕式前夕.由德国旅游运营商Avanti-Reisen组织的一次历经70天、以Setra S 415 HDH客车为交通工具、穿越德国、意大利、中国等9个国家的探险旅行团最终顺利抵达北京。
基金supported by the National Natural Science Foundation of China(grant numbers 61991421,61991424,and 61991420).
文摘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.
