基于自注意力特征融合的半监督生成对抗网络用于SAR目标识别  

Semi-supervised Generative Adversarial Network Based on Self-attention Feature Fusion for SAR Target Recognition

作　　者：应自炉 王发官 翟懿奎 王文琪 YING Zilu;WANG Faguan;ZHAI Yikui;WANG Wenqi(School of Intelligent Manufacturing,Wuyi University,Jiangmen,Guangdong 529020,China)

机构地区：[1]五邑大学智能制造学部,广东江门529020

出　　处：《信号处理》2022年第2期258-267,共10页Journal of Signal Processing

基　　金：广东省普通高校人工智能重点领域专项(2018KZDXM073,2020ZDZX3031,2019KZDZX1017);广东省科技厅国际科技合作项目(2021A0505030080);广东省基础与应用基础研究基金项目(2019A1515010716,2021A1515011576)。

摘　　要：与具有大量标注数据的光学图像相比,合成孔径雷达(Synthetic Aperture Radar,SAR)图像缺乏足够的标记样本,限制了监督学习的SAR目标识别算法的性能。而无监督识别方法又难以满足实际需求,因此本文提出了基于自注意力特征融合的半监督生成对抗网路。首先,在构建生成器和判别器时引入自注意力层,克服卷积算子不具有长距离特征提取的问题;其次,判别器采用多层级特征融合,捕获SAR图像的关键信息;最后,在训练过程中采用谱归一化,提升模型的收敛稳定性。为了验证所提方法的有效性,在运动与静止目标获取和识别(Moving and Stationary Target Acquisition and Recognition,MSTAR)数据集上进行了实验。实验结果表明,所提方法能从未标记样本中学习有价值的信息,有效地解决标注不足的问题。Compared with optical images with a large number of labeled data,synthetic aperture radar(SAR)images lacked sufficient labeled samples,which limited the performance of supervised learning SAR target recognition algorithm.However,the unsupervised recognition methods were difficult to meet the practical needs.This paper proposed a semi-supervised generative adversarial network based on self-attention feature fusion,alleviating the challenge of annotated data lacking.Firstly,the self-attention layer was introduced in the construction of generator and discriminator to overcome the problem that convolution did not have long range extraction capability.Secondly,the discriminator utilized multi-level feature fusion to capture the key information of SAR image.Finally,spectral normalization was applied in the training process to improve the convergence stability.In order to verify the effectiveness of the proposed method,experiments were carried out on Moving and Stationary Target Acquisition and Recognition(MSTAR)data sets.Experiment show that the proposed method can learn valuable information from unlabeled samples,effectively solve the problem of insufficient labeling.

关 键 词：合成孔径雷达 生成对抗网络 半监督 自注意力 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]