深度卷积神经网络在SAR自动目标识别领域的应用综述

Applications of Deep Convolutional Neural Network in SAR Automatic Target Recognition:a Summarization

深度卷积神经网络(DCNN)可自动学习目标层次化特征,在合成孔径雷达(SAR)自动目标识别(SAR-ATR)领域具有广泛应用前景。首先,介绍了DCNN的基本原理以及DCNN在光学图像上的应用与发展;然后,介绍了SAR-ATR的基本概念,综述了DCNN在SAR图像语义特征提取、片段级SAR图像分类、基于数据增强技术的SAR自动目标识别、异质图像变化检测等领域中的前沿应用研究及代表性网络架构;最后,总结并讨论了DCNN在SAR-ATR应用中存在的参数设置经验化、算法泛化能力较弱等不足,并对未来研究方向进行了展望。

Deep Convolutional Neural Network （ DCNN） can automatically learn the target＇ s hierarchical features,and it has wide application prospect in SAR-Automatic Target Recognition（ SAR-ATR）. Firstly, the basic principle of DCNN is introduced,and the application and development of DCNN in optical image are studied. Then,the basic concept of SAR-ATR is introduced,and the frontier application research and representative network architecture of DCNN in SAR image semantic feature extraction, frag ment - level S A R image classification,SAR automatic target recognition based on data enhancement technology, heterogene -ous image change detection are reviewed. Finally,the lack of parameter setting and the weak generalization ability of DCNN in SAR-ATR applications are summarized and discussed,and the future research direction is presented.