中北大学学报（自然科学版）2025，Vol.46Issue(5)：561-573,13.DOI:10.62756/jnuc.issn.1673-3193.2025.03.0012
基于多注意力特征融合的SAR图像目标分类算法
Target Classification Algorithm for SAR Images Based on Multi-Attention Feature Fusion
摘要
Abstract
A novel classification model for SAR image target classification,named Multi-Attention Feature Fusion Network(MAFNet)was proposed.Firstly,a multi-head self-attention mechanism was applied to the original image to capture global information.Secondly,a covariance attention mechanism was introduced to further enhance the representation of channel and spatial features.Thirdly,a shallow robust feature downs-ampling module was incorporated to more efficiently extract effective information from the raw image.Finally,the three attention-based features were fused to obtain more representative SAR image features.This approach overcomes the limitation of traditional convolutional neural networks,which only extract features within a local receptive field.By enhancing deep features in both channel and spatial dimensions and integrating features containing global information,the model significantly improves classification accuracy and robustness.Experimental results on the MSTAR dataset under the SOC condition show that MAFNet achieves a clas-sification accuracy of 99.96%,outperforming other existing algorithms.关键词
雷达图像目标分类/MSTAR数据集/注意力机制/特征提取Key words
SAR image classification/MSTAR dataset/attention mechanism/feature extraction分类
信息技术与安全科学引用本文复制引用
许丽龙,王春柳,侯宇超,王鹏..基于多注意力特征融合的SAR图像目标分类算法[J].中北大学学报（自然科学版）,2025,46(5):561-573,13.基金项目
山西省留学回国人员科技活动择优资助项目(20240011) （20240011）
山西省基础研究计划项目(202303021212164,202103021224195,202103021224212) （202303021212164,202103021224195,202103021224212）
山西省回国留学人员科研项目(2021-108) （2021-108）
