An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC

doi:10.1109/LGRS.2022.3183636

CASIA OpenIR > 复杂系统管理与控制国家重点实验室 > 影像分析与机器视觉

	An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC
	Dong, Yunyun 1; Liang, Chenbin2,3 ; Sun, Zengguo 4
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
	2022
卷号	19 页码:5
通讯作者	Sun, Zengguo(sunzg@snnu.edu.cn)
摘要	Image registration based on phase correlation has drawn extensive attention due to its high accuracy and efficiency. However, due to changes in image content, nonlinear gray difference, and other noises of image pairs, the line fitting of phase angle points acquired by the singular value decomposition (SVD) and 1-D phase unwrapping is also an intractable problem in the process of phase correlation image registration. In this letter, we propose a probability-guided random sample consensus (RANSAC), namely utilizing a probability to guide the hypothesis search of RANSAC to fit the line accurately and efficiently. The probability of each phase angle point is predicted by a deep convolution neural network (DCNN) of ProbNet we build and the parameters of the network are optimized effectively by integrating probability-guided RANSAC into an end-to-end trainable displacement estimation pipeline. The qualitative experiment is carried out to illustrate the effectiveness of the proposed method. In the quantitative experiments, two competitive methods of locally optimized RANSAC (LO-RANSAC) and least -square fitting (LSQ) and the naive RANSAC method are brought in to compare. The experimental result illustrates that the proposed method has an increase in the success rate of displacement estimation and efficiency.
关键词	Correlation Task analysis Training Convolution Remote sensing Image registration Neural networks Image registration phase correlation probability-guided random sample consensus (RANSAC)
DOI	10.1109/LGRS.2022.3183636
收录类别	SCI
语种	英语
资助项目	Program of the Natural Science Foundation of China[62001275] ; Program of the Central University Basic Research Fund of China[GK202003101]
项目资助者	Program of the Natural Science Foundation of China ; Program of the Central University Basic Research Fund of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000818886100007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49147
专题	复杂系统管理与控制国家重点实验室_影像分析与机器视觉
通讯作者	Sun, Zengguo
作者单位	1.Shaanxi Normal Univ, Northwest Land & Resource Res Ctr, Xian 710119, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Dong, Yunyun,Liang, Chenbin,Sun, Zengguo. An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA	Dong, Yunyun,Liang, Chenbin,&Sun, Zengguo.(2022).An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA	Dong, Yunyun,et al."An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.