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基于高层特征图组合及池化的高分辨率遥感图像检索

葛芸 马琳 江顺亮 叶发茂

葛芸, 马琳, 江顺亮, 叶发茂. 基于高层特征图组合及池化的高分辨率遥感图像检索[J]. 电子与信息学报, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017
引用本文: 葛芸, 马琳, 江顺亮, 叶发茂. 基于高层特征图组合及池化的高分辨率遥感图像检索[J]. 电子与信息学报, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017
Yun GE, Lin MA, Shunliang JIANG, Famao YE. The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017
Citation: Yun GE, Lin MA, Shunliang JIANG, Famao YE. The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017

基于高层特征图组合及池化的高分辨率遥感图像检索

doi: 10.11999/JEIT190017
基金项目: 国家自然科学基金(41801288, 41261091, 61662044, 61663031, 61762067)
详细信息
    作者简介:

    葛芸:女,1983年生,博士,讲师,研究方向为遥感图像处理与机器学习

    马琳:女,1996年生,硕士生,研究方向为遥感图像处理与机器学习

    江顺亮:男,1966年生,博士,教授,博士生导师,研究方向为算法设计与分析、计算机模拟与仿真、机器视觉

    叶发茂:男,1978年生,博士,副教授,研究方向为遥感图像处理、计算机图形学、机器学习

    通讯作者:

    葛芸 geyun@nchu.edu.cn

  • 中图分类号: TP751.1

The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval

Funds: The National Natural Science Foundation of China (41801288, 41261091, 61662044, 61663031, 61762067)
  • 摘要: 高分辨率遥感图像内容复杂,提取特征来准确地表达图像内容是提高检索性能的关键。卷积神经网络(CNN)迁移学习能力强,其高层特征能够有效迁移到高分辨率遥感图像中。为了充分利用高层特征的优点,该文提出一种基于高层特征图组合及池化的方法来融合不同CNN中的高层特征。首先将高层特征作为特殊的卷积层特征,进而在不同输入尺寸下保留高层输出的特征图;然后将不同高层输出的特征图组合成一个更大的特征图,以综合不同CNN学习到的特征;接着采用最大池化的方法对组合特征图进行压缩,提取特征图中的显著特征;最后,采用主成分分析(PCA)来降低显著特征的冗余度。实验结果表明,与现有检索方法相比,该方法提取的特征在检索效率和准确率上都有优势。
  • 图  1  图像检索流程

    图  2  融合高层特征

    图  3  UC-Merced中不同特征检索结果比较

    图  4  WHU-RS中不同特征检索结果比较

    图  5  不同CoP特征PCA降维结果

    表  1  不同输入图像尺寸下高层CNN特征的输出值

    输入图像尺寸fcG-pool5R-pool5
    默认尺寸1×1×40961×1×10241×1×2048
    256×256×3(UC-Merced)2×2×40962×2×10242×2×2048
    600×600×3(WHU-RS)13×13×409612×12×102413×13×2048
    下载: 导出CSV

    表  2  UC-Merced中特征的相关系数

    特征AM1619G
    M–0.0037
    160.00060.0028
    19–0.00230.00530.4817
    G0.0012–0.0063–0.0086–0.0100
    R–0.01000.0008–0.0060–0.00210.1175
    下载: 导出CSV

    表  3  WHU-RS中特征的相关系数

    特征AM1619G
    M–0.0080
    16–0.00090.0027
    190.00010.00510.4762
    G–0.0024–0.0038–0.0110–0.0093
    R–0.00450.0084–0.0069–0.00220.1138
    下载: 导出CSV

    表  4  不同输入尺寸CoP特征检索结果比较

    数据集特征默认尺寸原始尺寸
    ANMRRmAPANMRRmAP
    UC-MercedCoP(16_G)0.28980.64110.28800.6446
    CoP(16_G_M)0.28340.64850.28320.6504
    CoP(16_G_M_19)0.28340.64960.28050.6544
    WHU-RSCoP(A_16)0.20070.74660.23300.7116
    CoP(A_16_G)0.18910.75820.23190.7125
    CoP(A_16_G_19)0.18750.76100.23180.7124
    下载: 导出CSV

    表  5  UC-Merced中微调CoP特征检索结果比较

    数据集特征默认尺寸原始尺寸
    ANMRRmAPANMRRmAP
    UC-MercedCoP(16_G)-FT0.27380.66020.27770.6566
    CoP(16_G_M)-FT0.26420.67160.26780.6683
    CoP(16_G_M_19)-FT0.26040.67670.25610.6822
    WHU-RSCoP(A_16)-FT0.17230.78090.19750.7501
    CoP(A_16_G)-FT0.15820.79710.19240.7559
    CoP(A_16_G_19)-FT0.15190.80480.18790.7615
    下载: 导出CSV

    表  6  UC-Merced中CoP特征与其他特征检索结果比较

    特征ANMRR维数
    浅层特征VLAD[2]0.460416384
    3层图[4]0.4317
    CNN特征VGGM-fc[14]0.37804096
    VGGM-conv5-IFK[15]0.4580102400
    VGG16-fc[15]0.39404096
    VGG16-conv5-IFK[15]0.4070102400
    LDCNN[15]0.439030
    GoogLeNet-MultiPatch-FT[16]0.31401024
    GoogLeNet-MultiPatch-FT-PCA[16]0.285032
    CoP(16_G)0.28804096
    CoP(16_G_M_19)0.28054096
    CoP(16_G_M_19)-FT0.25614096
    CoP(16_G_M_19)-PCA0.257732
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-09
  • 修回日期:  2019-06-18
  • 网络出版日期:  2019-06-25
  • 刊出日期:  2019-10-01

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