Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph
文献类型:期刊论文
作者 | Zhao, Yong2; Liu, Guochen2; Xu, Shibiao1,3; Bu, Shuhui2; Jiang, Hongkai2; Wan, Gang4 |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
出版日期 | 2021-04-01 |
卷号 | 59期号:4页码:3502-3517 |
ISSN号 | 0196-2892 |
关键词 | Simultaneous localization and mapping Image reconstruction Optimization Real-time systems Global Positioning System Robustness Image fusion Aerial images digital orthophoto map (DOM) georeferenced low overlap mosaicing planar |
DOI | 10.1109/TGRS.2020.3008517 |
通讯作者 | Xu, Shibiao(shibiao.xu@nlpr.ia.ac.cn) ; Bu, Shuhui(bushuhui@nwpu.edu.cn) |
英文摘要 | Accurate digital orthophoto map generation from high-resolution aerial images is important in various applications. Compared with the existing commercial software and the current state-of-the-art mosaicing systems, a novel fast georeferenced orthophoto mosaicing framework is proposed in this study. The framework can adapt to the challenging requirements of high-accuracy orthoimage generations with relatively fast speed, even if the overlap rate is low. We provide appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching. On the basis of GPS information, we introduce an absolute position and rotation-averaging strategy for global pose initialization, which is essential for the high convergence and efficiency of nonconvex pose optimization of every image. We also propose a planar-restricted global pose graph optimization method. The optimization is extremely efficient and robust considering that point clouds are parameterized to planes. Finally, we apply a matching graph-based exposure compensation and region reduction algorithm for large-scale and high-resolution image fusion with high efficiency and novel precision. Experimental results demonstrate that our method can achieve the state-of-the-art performance while maintaining high precision and robustness. |
资助项目 | National Key Research and Development Program of China[2018YFB2100601] ; National Natural Science Foundation of China[91860124] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[51875459] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61573284] ; Aeronautical Science Foundation of China[20170253003] |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000633493700054 |
资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Aeronautical Science Foundation of China |
源URL | [http://ir.ia.ac.cn/handle/173211/44518] |
专题 | 模式识别国家重点实验室_三维可视计算 |
通讯作者 | Xu, Shibiao; Bu, Shuhui |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Northwestern Polytech Univ, Coll Aeronaut, Xian 710072, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Aerosp Engn Univ, Sch Aerosp Informat, Beijing 101416, Peoples R China |
推荐引用方式 GB/T 7714 | Zhao, Yong,Liu, Guochen,Xu, Shibiao,et al. Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(4):3502-3517. |
APA | Zhao, Yong,Liu, Guochen,Xu, Shibiao,Bu, Shuhui,Jiang, Hongkai,&Wan, Gang.(2021).Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(4),3502-3517. |
MLA | Zhao, Yong,et al."Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.4(2021):3502-3517. |
入库方式: OAI收割
来源:自动化研究所
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