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基于图注意力网络的半监督SAR舰船目标检测

吕进东 王彤 唐晓斌

吕进东, 王彤, 唐晓斌. 基于图注意力网络的半监督SAR舰船目标检测[J]. 电子与信息学报, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139
引用本文: 吕进东, 王彤, 唐晓斌. 基于图注意力网络的半监督SAR舰船目标检测[J]. 电子与信息学报, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139
LÜ Jindong, WANG Tong, TANG Xiaobin. Semi-supervised SAR Ship Target Detection with Graph Attention Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139
Citation: LÜ Jindong, WANG Tong, TANG Xiaobin. Semi-supervised SAR Ship Target Detection with Graph Attention Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139

基于图注意力网络的半监督SAR舰船目标检测

doi: 10.11999/JEIT220139
基金项目: 国家重点研发计划(2016YFE0200400)
详细信息
    作者简介:

    吕进东:男,工程师,博士生,研究方向为雷达系统及目标检测技术

    王彤:男,教授,研究方向为雷达成像及运动目标检测技术

    唐晓斌:女,研究员,研究方向为电磁信息技术

    通讯作者:

    吕进东 JDongLv@163.com

  • 中图分类号: TN957.51

Semi-supervised SAR Ship Target Detection with Graph Attention Network

Funds: The National Key R&D Program of China (2016YFE0200400)
  • 摘要: 基于深度学习的合成孔径雷达(SAR)舰船目标检测近年得到了快速发展。然而,传统有监督学习需要大量的标记样本来训练网络。针对此问题,该文提出一种基于图注意力网络(GAT)的半监督SAR舰船目标检测方法。首先,设计了对称卷积神经网络用于海陆分割。随后,完成超像素分割并将超像素块建模为GAT的节点,利用感兴趣区域池化层提取节点的多尺度特征。GAT采用注意力机制自适应地汇聚邻接节点特征实现对无标记节点的分类。最后,将预测为舰船目标的超像素块定位到SAR图像中并获得精细检测结果。在实测高分辨SAR图像数据集上验证了所提方法。结果表明该方法可以在少量标记样本下,以低虚警率实现对舰船目标的可靠检测。
  • 图  1  基于图注意力网络的半监督SAR舰船目标检测流程图

    图  2  基于特征选择性重建的海陆分割网络示意图

    图  3  多尺度特征提取示意图

    图  4  用于自适应计算遮掩标签的网络示意图

    图  5  基于特征选择性重建的海陆分割结果

    图  6  测试集部分图像的超像素分割结果

    图  7  SAR舰船目标检测结果对比

    7  SAR舰船目标检测结果对比(续图)

    表  1  不同尺度特征图提取的特征维度

    尺度特征图维度RoI池化尺寸$r$RoI输出特征维度不同尺度特征维度总特征维度
    尺度150×50×6422×2×642563072
    尺度2100×100×6422×2×64256
    尺度3200×200×3244×4×32512
    尺度4400×400×1688×8×161024
    尺度5800×800×13232×32×11024
    下载: 导出CSV
    算法1 同一类多个超像素块筛选流程
     输入:超像素块最小候选区域$ \left\{ {{\delta _i},i = 1,2, \cdots ,P} \right\} $,其中$P$为该类对应的超像素块个数。
     (1) 计算超像素块的亮度显著性$ \left\{ {{\eta _i}} \right\} $,其中$ {\eta _i} = {{{M_t}} \mathord{\left/ {\vphantom {{{M_t}} M}} \right. } M} $,$ {M_t} $为该超像素块中像素值高于门限$ {V_1} $的像素点数量。
     (2) 将$ \left\{ {{\eta _i}} \right\} $降序排列得到$ \left\{ {{\kappa _i}} \right\} $,若$ {{{\kappa _j}} \mathord{\left/ {\vphantom {{{\kappa _j}} {{\kappa _{j + 1}}}}} \right. } {{\kappa _{j + 1}}}} > {V_2} $,则剔除第$ j $个超像素块之后的所有超像素块,其中$ j = 1,2,\cdots,P - 1 $,$ {V_2} $为显著性门限。
     输出:筛选后的超像素块。
    下载: 导出CSV

    表  2  图注意力网络的详细建模信息

    图注意力网络有标记节点数无标记节点数总节点数标记率(%)
    G1-11 000 (舰船500,杂波500)4 4105 41018.48
    G1-2500 (舰船250,杂波250)4 91010.18
    G1-3200 (舰船100,杂波100)4 6104.34
    下载: 导出CSV

    表  3  测试集所有SAR图像舰船目标检测性能对比

    方法总舰船目标数漏警虚警
    GAT(标记率分别为18.48%, 10.18%和4.34%)766/7/91/1/2
    Faster R-CNN(训练样本分别为1000,500和200)9/10/122/1/2
    DCNN(训练样本分别为1000,500和200)8/8/99/15/15
    双参数CFAR-11415
    双参数CFAR-2913
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-15
  • 修回日期:  2022-06-21
  • 网络出版日期:  2022-06-30
  • 刊出日期:  2023-05-10

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