基于深度学习的合成孔径成像系统共相误差检测研究综述

doi:10.3969/j.issn.1007-5461.2022.06.007

量子电子学报 ›› 2022, Vol. 39 ›› Issue (6): 927-941.doi: 10.3969/j.issn.1007-5461.2022.06.007

• "光电探测与成像新技术及应用"专辑 • 上一篇下一篇

基于深度学习的合成孔径成像系统共相误差检测研究综述

马慧敏 1∗ , 檀磊 1 , 张京会2 , 张鹏飞3 , 宁孝梅 1 , 刘海秋 1 , 高彦伟 1

( 1 安徽农业大学信息与计算机学院, 安徽合肥 230036; 2 中国科学院合肥物质科学研究院安徽光学精密机械研究所, 中国科学院大气光学重点实验室, 安徽合肥 230031; 3 中国科学院合肥物质科学研究院智能机械研究所, 安徽合肥 230031 )

收稿日期:2022-07-03 修回日期:2022-07-22 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: E-mail: huiminma@ahau.edu.cn E-mail:huiminma@ahau.edu.cn
作者简介:马慧敏 ( 1984 - ), 女, 山西怀仁人, 博士, 副教授, 硕士生导师, 主要从事深度学习波前传感方面的研究。 E-mail: huiminma@ahau.edu.cn
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 61905002, 61805001), Open Research Fund of Key Laboratory of Atmospheric Optics, Chinese Academy of Sciences (中国科学院大气光学重点实验室开放课题基金, JJ-22-01), National Innovation and Entrepreneurship Training Program for College Students (国家级大学生创新创业训练计划项目, 202210364073)

Review of co-phasing error detection for synthetic aperture imaging system based on deep learning

MA Huimin 1∗ , TAN Lei 1 , ZHANG Jinghui 2 , ZHANG Pengfei 3 , NING Xiaomei 1 , LIU Haiqiu 1 , GAO Yanwei 1

( 1 School of Information and Computer, Anhui Agricultural University, Hefei 230036, China; 2 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China; 3 Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei 230031, China )

Received:2022-07-03 Revised:2022-07-22 Published:2022-11-28 Online:2022-12-14

摘要/Abstract

摘要： 光学合成孔径成像系统是利用多个小口径望远镜排列成稀疏孔径阵列来增大系统等效口径, 从而实现大口径光学系统的高分辨成像效果。子孔径间的共相误差探测是实现合成孔径系统高分辨率成像的重要前提, 该技术一直是该领域研究人员关注的焦点之一。新兴的人工智能及大数据技术为合成孔径成像系统共相误差探测提供了新思路和开辟了新方向。本文在简要回顾合成孔径成像系统共相误差探测方法的基础上, 介绍和分析了近年来深度学习技术在合成孔径成像系统共相误差探测方面的研究进展, 并对未来发展方向进行了总结和展望。

关键词: 大气光学, 合成孔径, 共相误差检测, 深度学习, 卷积神经网络

Abstract: In the optical synthetic aperture imaging system, multiple small aperture telescopes are arranged into a sparse aperture array to increase the equivalent aperture of the system, so as to achieve the high-resolution imaging effect of the large aperture optical system. The detection of co-phasing error between subapertures is an important prerequisite for realizing high-resolution imaging of synthetic aperture systems, and this technology has always been one of the focuses of researchers in this field. The emerging artificial intelligence and big data technology provide a new idea and open up a new direction for the detection of co-phasing error of synthetic aperture imaging system. On the basis of a brief review of the co-phasing error detection methods of synthetic aperture imaging system, the research progress of deep learning technology in co-phasing error detection of synthetic aperture imaging system in recent years is introduced and analyzed, and the future development direction is finally summarized and prospected.

Key words: tmospheric optics, synthetic aperture, co-phasing error detection, deep learning, convolutional neural network

中图分类号:

O439

马慧敏 ∗ , 檀磊 , 张京会 , 张鹏飞 , 宁孝梅 , 刘海秋 , 高彦伟 . 基于深度学习的合成孔径成像系统共相误差检测研究综述[J]. 量子电子学报, 2022, 39(6): 927-941.

MA Huimin ∗ , TAN Lei , ZHANG Jinghui , ZHANG Pengfei , NING Xiaomei , LIU Haiqiu , GAO Yanwei . Review of co-phasing error detection for synthetic aperture imaging system based on deep learning[J]. Chinese Journal of Quantum Electronics, 2022, 39(6): 927-941.

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基于深度学习的合成孔径成像系统共相误差检测研究综述

Review of co-phasing error detection for synthetic aperture imaging system based on deep learning

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