摘要
随着合成孔径雷达技术的不断发展,雷达图像目标识别成为重要的研究方向。近年来,深度学习技术在雷达图像目标检测与识别方面得到了广泛应用,然而,数据样本量少和数据样本类别不均衡成为制约深度学习在合成孔径雷达目标识别中的重要因素。对基于深度学习的SAR图像目标识别算法进行了分析,首先,介绍了SAR图像目标识别常用数据集和多角度SAR图像目标识别方法;然后,针对SAR图像目标识别中数据样本量少与样本类别不均衡问题分别进行了总结;最后,讨论了目前SAR图像目标识别仍然存在的问题和下一步的工作计划。
With the continuous development of Synthetic Aperture Radar(SAR) technology,radar image target recognition has become an important research direction.In recent years,Deep Learning(DL) technology has been widely used in radar image target detection and recognition.However,small data sample size and unbalanced data sample categories have become important factors limiting the application of DL to SAR image target recognition.The DL-based algorithms of SAR image target recognition are analyzed.Firstly,the common data sets for SAR image target recognition and multi-angle SAR image target recognition methods are introduced.Then,the problems of small data sample size and unbalanced sample categories in SAR image target recognition are summarized respectively.Finally,the remaining problems in SAR image target recognition and the research plan of the next step are discussed.
作者
李永刚
朱卫纲
LI Yonggang;ZHU Weigang(University of Aerospace Engineering,Graduate School,Beijing 101000,China;University of Aerospace Engineering,Department of Electronics and Optics,Beijing 101000,China)
出处
《电光与控制》
CSCD
北大核心
2022年第2期58-62,共5页
Electronics Optics & Control
基金
复杂电磁环境效应国家重点实验室项目(2020Z0203B)。
关键词
合成孔径雷达
SAR图像目标识别
数据样本量少
类别不均衡
Synthetic Aperture Radar(SAR)
SAR image target recognition
small data sample size
unbalanced categories
