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基于FlowS-Unet的遥感图像建筑物变化检测

顾炼 许诗起 竺乐庆

顾炼, 许诗起, 竺乐庆. 基于FlowS-Unet的遥感图像建筑物变化检测. 自动化学报, 2020, 46(6): 1291-1300. doi: 10.16383/j.aas.c180122
引用本文: 顾炼, 许诗起, 竺乐庆. 基于FlowS-Unet的遥感图像建筑物变化检测. 自动化学报, 2020, 46(6): 1291-1300. doi: 10.16383/j.aas.c180122
GU Lian, XU Shi-Qi, ZHU Le-Qing. Detection of Building Changes in Remote Sensing Images via FlowS-Unet. ACTA AUTOMATICA SINICA, 2020, 46(6): 1291-1300. doi: 10.16383/j.aas.c180122
Citation: GU Lian, XU Shi-Qi, ZHU Le-Qing. Detection of Building Changes in Remote Sensing Images via FlowS-Unet. ACTA AUTOMATICA SINICA, 2020, 46(6): 1291-1300. doi: 10.16383/j.aas.c180122

基于FlowS-Unet的遥感图像建筑物变化检测

doi: 10.16383/j.aas.c180122
基金项目: 

浙江省自然科学基金 LY20F020002

详细信息
    作者简介:

    顾炼  浙江工商大学计算机与信息工程学院硕士研究生.主要研究方向为图像处理, 模式识别. E-mail: guliancv@163.com

    许诗起  浙江工商大学计算机与信息工程学院硕士研究生.主要研究方向为数据挖掘, 深度学习. E-mail: xushiqitc@163.com

    通讯作者:

    竺乐庆  浙江工商大学计算机与信息工程学院副教授.主要研究方向为图像处理, 模式识别, 视频处理.本文通信作者. E-mail: zhuleqing@zjgsu.edu.cn

Detection of Building Changes in Remote Sensing Images via FlowS-Unet

Funds: 

Natural Science Foundation of Zhejiang Province LY20F020002

More Information
    Author Bio:

    GU Lian   Master student at the School of Computer and Information Engineering, Zhejiang Gongshang University. Her research interest covers image processing and pattern recognition

    XU Shi-Qi  Master student at the School of Computer and Information Engineering, Zhejiang Gongshang University. His research interest covers data mining and deep learning

    Corresponding author: ZHU Le-Qing  Associate professor at the School of Computer and Information Engineering, Zhejiang Gongshang University. Her research interest covers image processing, pattern recognition, and video processing. Corresponding author of this paper
  • 摘要: 针对目前人为探察土地资源利用情况的任务繁重、办事效率低下等问题, 提出了一种基于深度卷积神经网络的建筑物变化检测方法, 利用高分辨率遥感图像实时检测每个区域新建与扩建的建筑物, 以方便对土地资源进行有效管理.本文受超列(Hypercolumn)和FlowNet中的细化(Refinement)结构启发, 将细化和其他改进应用到U-Net, 提出FlowS-Unet网络.首先对遥感图像裁剪、去噪、标注语义制作数据集, 将该数据集划分为训练集和测试集, 对训练集进行数据增强, 并根据训练集图像的均值和方差对所有图像进行归一化; 然后将训练集输入集成了多尺度交叉训练、多重损失计算、Adam优化的全卷积神经网络FlowS-Unet中进行训练; 最后对网络模型的预测结果进行膨胀、腐蚀以及孔洞填充等后处理得到最终的分割结果.本文以人工分割结果为参考标准进行对比测试, 用FlowS-Unet检测得到的F1分数高达0.943, 明显优于FCN和U-Net的预测结果.实验结果表明, FlowS-Unet能够实时准确地将新建与扩建的建筑物变化检测出来, 并且该模型也可扩展到其他类似的图像检测问题中.
    Recommended by Associate Editor LIU Qing-Shan
    1)  本文责任编委 刘青山
  • 图  1  一种端到端的建筑物变化检测概览图

    Fig.  1  An overview of end-to-end architecture of building change detection

    图  2  整体方案实施流程

    Fig.  2  The schema of proposed approach

    图  3  部分卫星图原图

    Fig.  3  Part of the original satellite image

    图  4  减少拼接区域色差效果图

    Fig.  4  The result after the color difference of stitching blocks was sup-pressed

    图  5  去云、雾效果图

    Fig.  5  Results of cloud and fog removal

    图  6  人工标注示意图

    Fig.  6  The demonstration of manual label annotation

    图  7  FlowS-Unet网络结构

    Fig.  7  The network structure of FlowS-Unet

    图  8  后处理前后对比图

    Fig.  8  A comparison between the results before and after post-processing

    图  9  准确率与损失值曲线

    Fig.  9  Curves of accuracy and loss

    图  10  FlowS-Unet与现有方法的定性比较

    Fig.  10  A quality comparison of FlowS-Unet and previous methods

    表  1  FlowS-Unet与现有方法的性能比较

    Table  1  The performance comparison of FlowS-Unet

    序号方法F1分数(后处理前/后)时间(s)
    1FlowS-Unet0.933/0.94362
    2FCN0.858/0.87350
    3U-Net0.898/0.91359
    4人工标注1.00018 000
    下载: 导出CSV

    表  2  多尺度与单尺度训练及预测的F1分数比较

    Table  2  The F1 score comparison between multi-scale cross and single-scale training and testing

    训练尺度(像素)预测尺度(像素)F1分数(后处理前/后)
    2242240.903/0.923
    2562560.909/0.928
    2882880.913/0.931
    3203200.911/0.932
    2240.933/0.939
    2560.938/0.943
    多尺度2880.939/0.945
    3200.939/0.944
    多尺度平均0.942/0.946
    下载: 导出CSV

    表  3  FlowS-Unet与其他队伍的F1分数比较

    Table  3  The F1 score comparison of FlowS-Unet and other teams

    名次初赛复赛决赛
    第1名0.8900.9140.861
    第2名(FlowS-Unet)0.9030.8770.840
    第3名0.8670.8980.800
    第4名0.7060.8700.842
    第5名0.8790.9360.823
    下载: 导出CSV
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  • 收稿日期:  2018-03-05
  • 录用日期:  2018-07-15
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