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全局关系注意力引导场景约束的高分辨率遥感影像目标检测

张菁 吴鑫嘉 赵晓蕾 卓力 张洁

张菁, 吴鑫嘉, 赵晓蕾, 卓力, 张洁. 全局关系注意力引导场景约束的高分辨率遥感影像目标检测[J]. 电子与信息学报, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466
引用本文: 张菁, 吴鑫嘉, 赵晓蕾, 卓力, 张洁. 全局关系注意力引导场景约束的高分辨率遥感影像目标检测[J]. 电子与信息学报, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466
ZHANG Jing, WU Xinjia, ZHAO Xiaolei, ZHUO Li, ZHANG Jie. Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466
Citation: ZHANG Jing, WU Xinjia, ZHAO Xiaolei, ZHUO Li, ZHANG Jie. Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466

全局关系注意力引导场景约束的高分辨率遥感影像目标检测

doi: 10.11999/JEIT210466
基金项目: 国家自然科学基金(61370189),北京市教委-市基金联合资助项目(KZ201810005002), 北京市教育委员会科技计划一般项目(KM202110005027)
详细信息
    作者简介:

    张菁:女,1975年生,教授,研究方向为遥感影像内容分析与理解等

    吴鑫嘉:女,1998年生,硕士生,研究方向为遥感影像目标检测

    赵晓蕾:女,1995年生,硕士,研究方向为遥感影像目标检测

    卓力:女,1971年生,教授,研究方向为图像/视频信号处理

    张洁:女,1977年生,博士,研究方向为遥感数据处理

    通讯作者:

    张菁 zhj@bjut.edu.cn

  • 中图分类号: TN911.73; TP751.1

Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention

Funds: The National Natural Science Foundation of China (61370189), Beijing Municipal Education Commission Cooperation Beijing Natural Science Foundation (KZ201810005002), The General Program of Beijing Municipal Education Commission (KM202110005027)
  • 摘要: 高分辨率遥感影像中地物目标往往与所处场景类别息息相关,如能充分利用场景对地物目标的约束信息,有望进一步提升目标检测性能。考虑到场景信息和地物目标之间的关联关系,提出全局关系注意力(RGA)引导场景约束的高分辨率遥感影像目标检测方法。首先在多尺度特征融合检测器的基础网络之后,加入全局关系注意力学习全局场景特征;然后以学到的全局场景特征作为约束,结合方向响应卷积模块和多尺度特征模块进行目标预测;最后利用两个损失函数联合优化网络实现目标检测。在NWPU VHR-10数据集上进行了4组实验,在场景信息约束的条件下取得了更好的目标检测性能。
  • 图  1  全局关系引导场景约束的高分辨率遥感影像目标检测方法(OR-FS-SSD+RGA)

    图  2  全局关系注意力结构

    图  3  全局空间关系注意力

    图  4  全局通道关系注意力

    图  5  不同注意力模块的检测结果

    图  6  4个网络中每类目标检测准确率

    图  7  FSSD, FS-SSD, Faster-RCNN, OR-FS-SSD+CA和OR-FS-SSD+RGA-S的主观结果对比

    表  1  OR-FS-SSD+RGA最终预测特征图尺寸

    Pred1Pred2Pred3Pred4Pred5Pred6Pred_avg
    64×6432×3216×168×84×42×216×16
    下载: 导出CSV

    表  2  网络超参数设置

    迭代次数学习率批处理大小动量权重衰减
    1500.001120.90.005
    下载: 导出CSV

    表  3  和主流网络的检测准确率对比

    网络mAP (%)FPS
    Faster-RCNN93.100.09
    YOLOv391.0414.68
    OR-FS-SSD+CA[6]94.7429.57
    LCFFN[14]93.670.35
    GBD[15]93.952.20
    CBD-E[16]94.982.00
    ORSIm[17]95.394.72
    OR-FS-SSD+RGA-S (本文)95.5930.07
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-25
  • 修回日期:  2021-09-01
  • 网络出版日期:  2022-04-13
  • 刊出日期:  2022-08-17

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