基于卷积神经网络的SAR图像目标识别研究

田壮壮; 占荣辉; 胡杰民; 张军

doi:10.12000/JR16037

基于卷积神经网络的SAR图像目标识别研究

DOI: 10.12000/JR16037

(国防科学技术大学自动目标识别重点实验室长沙 410073)

基金项目:

国家自然科学基金(61471370)

详细信息

作者简介:
田壮壮(1993-),男,硕士生,研究方向为雷达目标识别。E-mail:tzz14@nudt.edu.cn;占荣辉(1978-),男,讲师,博士,研究方向为雷达目标识别、雷达信息处理;胡杰民(1983-),男,讲师,博士,研究方向为空间目标识别、雷达成像;张军(1973-),男,研究员,博士,研究方向为雷达智能信号处理、制导雷达应用技术。

通讯作者:
田壮壮tzz14@nudt.edu.cn

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出版历程
- 收稿日期: 2016-02-03
- 修回日期: 2016-03-15
- 网络出版日期: 2016-06-28

SAR ATR Based on Convolutional Neural Network

ATR Key Laboratory, National University of Defense Technology, Changsha 410073, China

Funds:

The National Natural Science Foundation of China (61471370)

摘要

摘要: 针对合成孔径雷达(Synthetic Aperture Radar, SAR)的图像目标识别应用, 该文提出了一种基于卷积神经网络(Convolutional Neural Network, CNN)的SAR图像目标识别方法。首先通过在误差代价函数中引入类别可分性度量, 提高了卷积神经网络的类别区分能力；然后利用改进后的卷积神经网络对SAR图像进行特征提取；最后利用支持向量机(Support Vector Machine, SVM)对特征进行分类。使用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition, MSTAR)SAR图像数据进行实验, 识别结果证明了所提方法的有效性。
- 合成孔径雷达 /
- 自动目标识别 /
- 卷积神经网络 /
- 支持向量机 /
- BP算法
Abstract: This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network's ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
- Synthetic Aperture Radar (SAR) /
- Automatic Target Recognition (ATR) /
- Convolutional Neural Network (CNN) /
- Support Vector Machine (SVM) /
- Back Propagation (BP)

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参考文献(18)

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