电子学报 ›› 2021, Vol. 49 ›› Issue (9): 1665-1674.DOI: 10.12263/DZXB.20200486

• 学术论文 •    下一篇

基于注意力机制特征融合网络的SAR图像飞机目标快速检测

赵琰, 赵凌君, 匡纲要   

  1. 国防科技大学电子信息系统与复杂电磁环境效应国家重点实验室,湖南 长沙 410073
  • 收稿日期:2020-05-21 修回日期:2020-11-30 出版日期:2021-09-25 发布日期:2021-09-25
  • 作者简介:赵 琰 男,1996年出生于陕西西安,国防科技大学电子科学学院在读博士研究生,研究方向为遥感信息处理,合成孔径雷达目标自动识别. E-mail:zy34731@qq.com
    赵凌君(通信作者) 女,1981年出生于安徽安庆,国防科技大学电子科学学院副教授,研究方向为遥感信息处理,合成孔径雷达目标自动识别.E-mail:zhaolingjunkd@126.com
    匡纲要 男,1966年出生于湖南衡阳,国防科技大学电子科学学院电子信息系统与复杂电磁环境效应(CEMEE)国家重点实验室教授,博士生导师,研究方向为遥感图像智能解译、SAR图像目标检测与识别. E-mail:kuangmerg@hotmail.com
  • 基金资助:
    国家自然科学基金(61971426)

Attention Feature Fusion Network for Rapid Aircraft Detection in SAR Images

Yan ZHAO, Ling-jun ZHAO, Gang-yao KUANG   

  1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,National University of Defense Technology,Changsha,Hunan 410073,China
  • Received:2020-05-21 Revised:2020-11-30 Online:2021-09-25 Published:2021-09-25

摘要:

针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像中飞机目标散射点离散化程度高,周围背景干扰复杂,现有算法对飞机浅层语义特征表征能力弱等问题,本文提出了基于注意力特征融合网络(Attention Feature Fusion Network,AFFN)的SAR 图像飞机目标检测算法.通过引入瓶颈注意力模块(Bottleneck Attention Module,BAM),本文在AFFN中构建了包含注意力双向特征融合模块(Attention Bidirectional Feature Fusion Module,ABFFM)与注意力传输连接模块(Attention Transfer Connection Block,ATCB)的注意力特征融合策略并合理优化了网络结构,提升了算法对飞机离散化散射点浅层语义特征的提取与判别.基于自建的Gaofen-3 与TerraSAR-X 卫星图像混合飞机目标实测数据集,实验对AFFN与基于深度学习的通用目标检测以及SAR图像特定目标检测算法进行了比较,其结果验证了AFFN对SAR图像飞机目标检测的准确性与高效性.

关键词: 注意力机制, 特征融合, 飞机目标快速检测, SAR图像, 卷积神经网络

Abstract:

Aiming at the problems of high discretization of aircraft’s backscattering points, complex background interference of surroundings in Synthetic Aperture Radar (SAR) images and weak representation of shallow semantic features of aircraft by existing algorithms, an Attention Feature Fusion Network (AFFN) was proposed for aircraft detection in SAR images. By introducing Bottleneck Attention Module (BAM), this article constructed an attention feature fusion strategy consisting of Attention Bidirectional Feature Fusion Module (ABFFM) and Attention Transfer Connection Block (ATCB) in AFFN, and rationally optimized the network structure so as to strengthen the abilities of extracting and discriminating shallow semantic features of aircraft. Based on a self-built Gaofen-3 and TerraSAR-X mixed aircraft dataset, AFFN was compared with several CNN-based general object detection methods and methods designed for specific objects in SAR images. The experimental results illustrated the accuracy and effectiveness of our method for aircraft detection in SAR images

Key words: attention feature fusion network (AFFN), feature fusion, rapid aircraft detection, synthetic aperture radar (SAR) images, convolutional neural network (CNN)

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