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用于遥感图像变化检测的全尺度特征聚合网络

刘国强 房胜 李哲

刘国强, 房胜, 李哲等 . 用于遥感图像变化检测的全尺度特征聚合网络[J]. 北京航空航天大学学报, 2022, 48(8): 1464-1470. doi: 10.13700/j.bh.1001-5965.2021.0522
引用本文: 刘国强, 房胜, 李哲等 . 用于遥感图像变化检测的全尺度特征聚合网络[J]. 北京航空航天大学学报, 2022, 48(8): 1464-1470. doi: 10.13700/j.bh.1001-5965.2021.0522
LIU Guoqiang, FANG Sheng, LI Zheet al. A full-scale feature aggregation network for remote sensing image change detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1464-1470. doi: 10.13700/j.bh.1001-5965.2021.0522(in Chinese)
Citation: LIU Guoqiang, FANG Sheng, LI Zheet al. A full-scale feature aggregation network for remote sensing image change detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1464-1470. doi: 10.13700/j.bh.1001-5965.2021.0522(in Chinese)

用于遥感图像变化检测的全尺度特征聚合网络

doi: 10.13700/j.bh.1001-5965.2021.0522
基金项目: 

山东省自然科学基金 ZR2020MF132

详细信息
    通讯作者:

    房胜, E-mail: fangsheng@tsinghua.org.cn

  • 中图分类号: TP751

A full-scale feature aggregation network for remote sensing image change detection

Funds: 

Shandong Provincial Natural Science Foundation ZR2020MF132

More Information
  • 摘要:

    变化检测(CD)是遥感的一项重要任务,通常面临许多伪变化和较大的尺度变化。目前的方法主要侧重于对差异特征的建模,忽略了从原始图像中提取足够的信息,影响了特征的识别能力,难以稳定地区分出变化区域。针对以上问题,提出了一种全尺度特征聚合网络(FFANet)来更充分地利用原始图像特征,促使生成的特征表示在语义上更丰富、在空间上更准确,从而提高了网络对小目标和目标边缘的检测性能。同时,拓展了深监督来结合多尺度的预测图,以促使不同对象在更合适的尺度上进行检测,从而提升了网络对对象尺度变化的鲁棒性。在CDD数据集上,相比于基线网络,所提方法仅增加了1.01×106的参数量,就将F1分数提升了0.034。

     

  • 图 1  FFANet的网络结构

    Figure 1.  Network architecture of FFANet

    图 2  用于全尺度特征融合的编码器

    Figure 2.  Encoders for full-scale feature fusion

    图 3  特征图S3(N)的构建

    Figure 3.  Construction of feature map S3(N)

    图 4  用于生成多尺度差异特征的解码器

    Figure 4.  Decoder for generating multi-scale difference feature

    图 5  多尺度分类器

    Figure 5.  Multi-scale classifier

    图 6  CDD测试集上不同方法的可视化

    Figure 6.  Visualization of different methods on CDD test set

    图 7  普通的编码器

    Figure 7.  Plain encoder

    图 8  编码器和分类器的对比实验

    Figure 8.  Comparison experiments of encoder and classifier

    表  1  CDD和LEVIR数据集上FFANet与其他方法的对比

    Table  1.   Comparison of FFANet with other methods on CDD and LEVIR datasets

    方法 参数量/106 计算量/GFLOPs CDD LEVIR
    P R F1 P R F1
    FC-EF 1.35 7.14 0.749 0.494 0.595 0.754 0.730 0.742
    FC-Siam-conc 1.55 10.64 0.779 0.622 0.692 0.852 0.736 0.790
    FC-Siam-diff 1.35 9.44 0.786 0.588 0.673 0.861 0.687 0.764
    IFN 35.72 164.53 0.950 0.861 0.903 0.903 0.876 0.889
    DASNet 16.25 113.09 0.914 0.925 0.919 0.811 0.788 0.799
    SNUNet 12.03 109.62 0.956 0.949 0.953 0.889 0.874 0.881
    FFANet 8.64 28.81 0.962 0.957 0.960 0.925 0.892 0.908
    注:GFLOPs指109次浮点运算。
    下载: 导出CSV

    表  2  CDD数据集上的消融实验

    Table  2.   Ablation experiments on CDD data set

    序号 编码器 分类器 参数量/106 P R F1
    × × 7.63 0.955 0.900 0.926
    × 8.64 0.960 0.942 0.951
    × 7.63 0.957 0.943 0.950
    8.64 0.962 0.957 0.960
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-10-01
  • 刊出日期:  2021-10-29

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