基于散射信息和元学习的SAR图像飞机目标识别

吕艺璇 王智睿 王佩瑾 李盛阳 谭洪 陈凯强 赵良瑾 孙显

吕艺璇, 王智睿, 王佩瑾, 等. 基于散射信息和元学习的SAR图像飞机目标识别[J]. 雷达学报, 2022, 11(4): 652–665. doi: 10.12000/JR22044
引用本文: 吕艺璇, 王智睿, 王佩瑾, 等. 基于散射信息和元学习的SAR图像飞机目标识别[J]. 雷达学报, 2022, 11(4): 652–665. doi: 10.12000/JR22044
LYU Yixuan, WANG Zhirui, WANG Peijin, et al. Scattering information and meta-learning based SAR images interpretation for aircraft target recognition[J]. Journal of Radars, 2022, 11(4): 652–665. doi: 10.12000/JR22044
Citation: LYU Yixuan, WANG Zhirui, WANG Peijin, et al. Scattering information and meta-learning based SAR images interpretation for aircraft target recognition[J]. Journal of Radars, 2022, 11(4): 652–665. doi: 10.12000/JR22044

基于散射信息和元学习的SAR图像飞机目标识别

doi: 10.12000/JR22044
基金项目: 国家自然科学基金(61725105, 62076241)
详细信息
    作者简介:

    吕艺璇(1997–),女,中国科学院大学信息与通信工程专业硕士研究生,2019年获得西安电子科技大学学士学位,主要研究方向为计算机视觉与SAR图像智能解译,小样本SAR图像识别等

    王智睿(1990–),男,2018年获得清华大学博士学位,现为中国科学院空天信息创新研究院助理研究员,主要研究方向为SAR图像智能解译

    王佩瑾(1996–),女,中国科学院空天信息创新研究院研究实习员,主要研究方向为遥感图像智能解译技术

    孙 显(1981–),男,中国科学院空天信息创新研究院研究员,博士生导师,IEEE高级会员,《雷达学报》青年编委,主要研究方向为计算机视觉与遥感图像理解

    通讯作者:

    孙显 sunxian@mail.ie.ac.cn

  • 责任主编:徐丰 Corresponding Editor: XU Feng
  • 中图分类号: TP753

Scattering Information and Meta-learning Based SAR Images Interpretation for Aircraft Target Recognition

Funds: The National Natural Science Foundation of China (61725105, 62076241)
More Information
  • 摘要: SAR图像由于数据获取难度大,样本标注难,目标覆盖率不足,导致包含地理空间目标的影像数量稀少。为了解决这些问题,该文开展了基于散射信息和元学习的SAR图像飞机目标识别方法研究。针对SAR图像中不同型号飞机空间结构离散分布差异较大的情况,设计散射关联分类器,对飞机目标的离散程度量化建模,通过不同目标离散分布的差异来动态调整样本对的权重,指导网络学习更具有区分性的类间特征表示。考虑到SAR目标成像易受背景噪声的影响,设计了自适应特征细化模块,促使网络更加关注飞机的关键部件区域,减少背景噪声干扰。该文方法有效地将目标散射分布特性与网络的自动学习过程相结合。实验结果表明,在5-way 1-shot的极少样本新类别识别任务上,该方法识别精度为59.90%,相比于基础方法提升了3.85%。减少一半训练数据量后,该方法在新类别的极少样本识别任务上仍然表现优异。

     

  • 图  1  5-way 1-shot设置

    Figure  1.  The 5-way 1-shot setup

    图  2  不同机型的散射点提取结果

    Figure  2.  Scattering point extraction results of different models

    图  3  不同机型不同聚类点数下的散射点提取结果

    Figure  3.  Scattering point extraction results for different models and different clustering points

    图  4  不同朝向的同型号飞机对应的离散因子

    Figure  4.  Discrete factors corresponding to the same type of aircraft with different orientations

    图  5  本文方法的整体结构

    Figure  5.  The overall structure of the method in this paper

    图  6  散射关联分类器的网络结构

    Figure  6.  Network structure of scattering association classifier

    图  7  注意力嵌入模块网络结构

    Figure  7.  The attention embedding module network structure

    图  8  SAR-ACD中的民用飞机及其对应的光学图像

    Figure  8.  Civil aircraft in SAR-ACD and their corresponding optical images

    图  9  各型号民用飞机的原始数量

    Figure  9.  The original quantity of each type of civil aircraft

    图  10  不同方法对应的卷积层输出特征图可视化

    Figure  10.  Visualization of corresponding layer feature map of different methods

    图  11  不同训练数据占比时的测试准确率

    Figure  11.  Test accuracy with different proportions of training data

    图  12  预训练阶段基本原理和流程展示

    Figure  12.  The basic principle and process display of the pre-training stage

    图  13  5-way 1-shot条件下在元测试数据集上不同模块的混淆矩阵

    Figure  13.  Confusion matrix on meta-test dataset on the 5-way 1-shot condition

    图  14  5-way 1-shot条件下不同方法模块的训练损失变化曲线

    Figure  14.  The loss curve with different modules during training on the 5-way 1-shot condition

    图  15  大场景复杂机场下的识别性能测试

    Figure  15.  Recognition performance test under large scene and complex airport

    表  1  实验中训练和测试期间数据设置

    Table  1.   Data setup during training and testing

    设置5-way 5-shot5-way 1-shot
    支持集查询集支持集查询集
    元训练515115
    元测试515115
    下载: 导出CSV

    表  2  自适应特征细化模块和散射关联分类器的消融实验

    Table  2.   Ablation study on adaptive feature refinement module and scattering association module

    散射关联分类器自适应特征细化模块5-way 1-shot (%)5-way 5-shot (%)
    ××56.05 ± 0.866.71 ± 0.7
    ×57.57 ± 0.967.80 ± 0.7
    ×58.31 ± 0.968.20 ± 0.7
    58.43 ± 0.868.52 ± 0.7
    下载: 导出CSV

    表  3  结合预训练过程的消融实验

    Table  3.   Ablation study combined with the pre-training process

    散射关联分类器自适应特征细化模块5-way 1-shot (%)5-way 5-shot (%)
    ××57.33 ± 0.967.60 ± 0.7
    ×58.86 ± 0.868.72 ± 0.7
    ×59.60 ± 0.968.85 ± 0.7
    59.90 ± 0.970.13 ± 0.7
    下载: 导出CSV

    表  4  本文方法和其他方法的识别精度对比

    Table  4.   Comparison of recognition accuracy between our method and other methods

    模型5-way 1-shot
    准确率(%)
    5-way 5-shot
    准确率(%)
    KNN22.5036.30
    MAML[32]52.2067.50
    MatchingNet[33]54.5458.90
    PrototypicalNet[34]53.1868.66
    MSAR[26]56.5765.15
    ResNet50[31]+微调29.6542.30
    本文散射辅助方法59.9070.13
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-11
  • 修回日期:  2022-04-12
  • 网络出版日期:  2022-04-29
  • 刊出日期:  2022-08-28

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