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基于三元采样图卷积网络的半监督遥感图像检索

冯孝鑫 王子健 吴奇

冯孝鑫, 王子健, 吴奇. 基于三元采样图卷积网络的半监督遥感图像检索[J]. 电子与信息学报, 2023, 45(2): 644-653. doi: 10.11999/JEIT211478
引用本文: 冯孝鑫, 王子健, 吴奇. 基于三元采样图卷积网络的半监督遥感图像检索[J]. 电子与信息学报, 2023, 45(2): 644-653. doi: 10.11999/JEIT211478
FENG Xiaoxin, WANG Zijian, WU Qi. Semi-supervised Learning Remote Sensing Image Retrieval Method Based on Triplet Sampling Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2023, 45(2): 644-653. doi: 10.11999/JEIT211478
Citation: FENG Xiaoxin, WANG Zijian, WU Qi. Semi-supervised Learning Remote Sensing Image Retrieval Method Based on Triplet Sampling Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2023, 45(2): 644-653. doi: 10.11999/JEIT211478

基于三元采样图卷积网络的半监督遥感图像检索

doi: 10.11999/JEIT211478
基金项目: 国家自然科学基金(U1933125)
详细信息
    作者简介:

    冯孝鑫:男,硕士,研究方向为机器学习、计算机视觉

    王子健:男,博士生,研究方向为统计机器学习

    吴奇:男,研究员,研究方向为脑认知、机器学习

    通讯作者:

    吴奇 wuqi7812@sjtu.edu.cn

  • 中图分类号: TN911.73; TP181

Semi-supervised Learning Remote Sensing Image Retrieval Method Based on Triplet Sampling Graph Convolutional Network

Funds: The National Natural Science Foundation of China (U1933125)
  • 摘要: 该文提出了一种基于三元采样图卷积网络的度量学习方法,以实现遥感图像的半监督检索。所提方法由三元图卷积网络(TGCN)和基于图的三元组采样(GTS)两部分组成。TGCN由3个具有共享权重的并行卷积神经网络和图卷积网络组成,用以提取图像的初始特征以及学习图像的图嵌入。通过同时学习图像特征以及图嵌入,TGCN能够得到用于半监督图像检索的有效图结构。接着,通过提出的GTS算法对图结构内隐含的图像相似性信息进行评价,以选择合适的困难三元组(Hard Triplet),并利用困难三元组组成的样本集合对模型进行有效快速的模型训练。通过TGCN和GTS的组合,提出的度量学习方法在两个遥感数据集上进行了测试。实验结果表明,TGCN-GTS具有以下两方面的优越性:TGCN能够根据图像及图结构学习到有效的图嵌入特征及度量空间;GTS有效评估图结构内隐含的图像相似性信息选择合适的困难三元组,显著提升了半监督遥感图像检索效果。
  • 图  1  三元组与度量学习

    图  2  三元采样图卷积网络

    图  3  图构建方式

    图  4  图像检索结果对比

    图  5  triplet样本t-SNE 2维映射图

    表  1  各算法在AID和NWPU-RESISC45数据集上的图像检索mAP@40

    MethodAIDNWPU-RESISC45
    LTDR 5%LTDR 10%LTDR 20%LTDR 5%LTDR 10%LTDR 20%
    DML0.72090.79130.87790.70410.75940.8111
    D-CNN0.75210.82370.91490.75020.82290.8843
    SNCA0.78320.82650.92750.81430.84320.9091
    HRS2DML0.78760.84900.90600.80440.85050.9010
    TGCN-RTS0.79130.85760.89280.83690.88720.9044
    TGCN-GTS0.90210.94370.97120.92410.95800.9648
    下载: 导出CSV

    表  2  算法模型复杂度比较

    方法${\rm{NP}}\left(\times {10}^{6}\right)$${\rm{FLOPS}}(\times {10}^{9})$
    DML11.20937.1258
    TGCN-RTS11.72797.2989
    TGCN-GTS11.72797.7584
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-20
  • 修回日期:  2022-04-28
  • 网络出版日期:  2022-06-28
  • 刊出日期:  2023-02-07

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