跨卷积网络特征融合的SAR图像目标识别

doi:10.16182/j.issn1004731x.joss.19-0609

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (3): 554-561.doi: 10.16182/j.issn1004731x.joss.19-0609

跨卷积网络特征融合的SAR图像目标识别

冯新扬, 邵超

河南财经政法大学计算机与信息工程学院,河南郑州 450046

收稿日期:2019-11-25 修回日期:2020-05-18 出版日期:2021-03-18 发布日期:2021-03-18
作者简介:冯新扬(1980-),男,博士,讲师,研究方向为软件工程,大数据处理。E-mail：hermit2005@sina.com
基金资助:
国家自然科学基金(61202285)

SAR Image Target Recognition Based on Across Convolution Network Feature Fusion

Feng Xinyang, Shao Chao

School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, China

Received:2019-11-25 Revised:2020-05-18 Online:2021-03-18 Published:2021-03-18

摘要/Abstract

摘要： 卷积神经网络(Convolutional Neural Network,CNN)在合成孔径雷达(Synthetic Aperture Radar,SAR)图像目标识别领域得到广泛应用。在LeNet-5神经网络模型的基础上,提出了跨卷积网络特征融合的SAR图像识别方法。利用MNIST手写数据对LeNet-5网络参数进行初始化,提取SAR图像的深层特征和浅层特征,对浅层特征进行主成分分析以得到关键类别信息,将深层特征和浅层特征进行融合,使用协作表示分类(Collaborative Representation Classification, CRC)将融合的两部分进行识别。通过公开数据集的实验验证表明,在不扩充训练样本条件下,该方法可达到98%的平均识别率。

关键词: 合成孔径雷达, LeNet-5神经网络, 协作表示分类, 深层特征

Abstract: Convolutional neural networks have been widely used in the field of synthetic aperture radar image target recognition. Based on the LeNet-5 neural network model, a SAR image target recognition method are initialized across convolution network feature fusion is proposed. The LeNet-5 network parameters on the basis of MNIST handwritten data. The deep and shallow features of the SAR image are extracted, and the principal component analysis on the shallow features is performed to obtain key category information. Deep features and shallow features are fused and are classified and recognised by sent to collaborative representation. Experimental results show that the method can achieve 98% average recognition rate without expanding the training samples.

Key words: synthetic aperture radar, LeNet-5 neural network, collaborative representation classification, deep feature

中图分类号:

TP183
TP391

冯新扬, 邵超. 跨卷积网络特征融合的SAR图像目标识别[J]. 系统仿真学报, 2021, 33(3): 554-561.

Feng Xinyang, Shao Chao. SAR Image Target Recognition Based on Across Convolution Network Feature Fusion[J]. Journal of System Simulation, 2021, 33(3): 554-561.

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跨卷积网络特征融合的SAR图像目标识别

SAR Image Target Recognition Based on Across Convolution Network Feature Fusion

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