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Efficient Region-Based 3-D Urban Building Reconstruction From TomoSAR Images

文献类型：期刊论文

作者Wang, Wei1; Yu, Liankun2; Dong, Qiulei2,3; Hu, Zhanyi2,3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2024
卷号62页码:14
关键词3-Dreconstruction geometric primitive plane fitting structure prior tomographic synthetic aperture radar (TomoSAR) 3-Dreconstruction geometric primitive plane fitting structure prior tomographic synthetic aperture radar (TomoSAR)
ISSN号0196-2892
DOI10.1109/TGRS.2024.3417948
通讯作者Dong, Qiulei(qldong@nlpr.ia.ac.cn) ; Hu, Zhanyi(huzy@nlpr.ia.ac.cn)
英文摘要The tomographic synthetic aperture radar (TomoSAR) technique has been gaining attention because it can retrieve the 3-D structures of urban buildings by synthesizing apertures along the elevation direction. However, most existing TomoSAR methods in literature calculate elevations pixel by pixel and overlook the correlation between elevations, leading to low accuracy and efficiency. To solve these problems, this study introduces an efficient region-based 3-D urban building reconstruction method that incorporates different geometric primitives (i.e., points, planes, and models). Specifically, the proposed method, under the constraints constructed by different geometric primitives, follows three steps to reconstruct the box-like models of urban buildings: 1) it detects double-bounce regions and reconstructs box-like models based on plane sweeping and region growing; 2) it reconstructs box-like models based on multiplane fitting and optimization for nondouble-bounce regions; and 3) it regularizes box-like models based on building layout priors (e.g., collinearity and proximity). The experimental results on two datasets show that the proposed method can efficiently produce reliable results and outperforms several existing methods both qualitatively and quantitatively.
WOS关键词SAR TOMOGRAPHY
资助项目National Natural Science Foundation of China[61991423] ; National Natural Science Foundation of China[U1805264] ; Key Scientific andTechnological Project of Henan Province[232102321068] ; Research and Practice Project on Teaching Reform of Higher Education in Henan Province[2024SJGLX0465]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001270564700013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Key Scientific andTechnological Project of Henan Province ; Research and Practice Project on Teaching Reform of Higher Education in Henan Province
源URL[http://ir.ia.ac.cn/handle/173211/59266]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Dong, Qiulei; Hu, Zhanyi
作者单位1.Zhoukou Normal Univ, Sch Network Engn, Zhoukou 466000, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Wei,Yu, Liankun,Dong, Qiulei,et al. Efficient Region-Based 3-D Urban Building Reconstruction From TomoSAR Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:14.
APA Wang, Wei,Yu, Liankun,Dong, Qiulei,&Hu, Zhanyi.(2024).Efficient Region-Based 3-D Urban Building Reconstruction From TomoSAR Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,14.
MLA Wang, Wei,et al."Efficient Region-Based 3-D Urban Building Reconstruction From TomoSAR Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):14.

入库方式： OAI收割

来源：自动化研究所

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