Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph

被引:7
|
作者
Zhao, Yong [1 ]
Liu, Guochen [1 ]
Xu, Shibiao [2 ,3 ]
Bu, Shuhui [1 ]
Jiang, Hongkai [1 ]
Wan, Gang [4 ]
机构
[1] Northwestern Polytech Univ, Coll Aeronaut, Xian 710072, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
[4] Aerosp Engn Univ, Sch Aerosp Informat, Beijing 101416, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2021年 / 59卷 / 04期
基金
中国国家自然科学基金;
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;
D O I
10.1109/TGRS.2020.3008517
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
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.
引用
收藏
页码:3502 / 3517
页数:16
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