中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统管理与控制国家重点实验室
  5. 影像分析与机器视觉团队
An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC

文献类型：期刊论文

作者Dong, Yunyun4; Liang, Chenbin2,3; Sun, Zengguo1
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期2022
卷号19页码:5
关键词Correlation Task analysis Training Convolution Remote sensing Image registration Neural networks Image registration phase correlation probability-guided random sample consensus (RANSAC)
ISSN号1545-598X
DOI10.1109/LGRS.2022.3183636
通讯作者Sun, Zengguo(sunzg@snnu.edu.cn)
英文摘要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.
资助项目Program of the Natural Science Foundation of China[62001275] ; Program of the Central University Basic Research Fund of China[GK202003101]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000818886100007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Program of the Natural Science Foundation of China ; Program of the Central University Basic Research Fund of China
源URL[http://ir.ia.ac.cn/handle/173211/49146]  
专题自动化研究所_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团队
通讯作者Sun, Zengguo
作者单位1.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Shaanxi Normal Univ, Northwest Land & Resource Res Ctr, 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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。