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针对遥感图像大棚提取的深度学习模型研究

宋灿 吴谨 朱磊 邓慧萍

宋灿, 吴谨, 朱磊, 邓慧萍. 针对遥感图像大棚提取的深度学习模型研究[J]. 微电子学与计算机, 2021, 38(1): 51-56.
引用本文: 宋灿, 吴谨, 朱磊, 邓慧萍. 针对遥感图像大棚提取的深度学习模型研究[J]. 微电子学与计算机, 2021, 38(1): 51-56.
SONG Can, WU Jin, ZHU Lei, DENG Hui-ping. Greenhouse segmentation of remote sensing images based on deep learning[J]. Microelectronics & Computer, 2021, 38(1): 51-56.
Citation: SONG Can, WU Jin, ZHU Lei, DENG Hui-ping. Greenhouse segmentation of remote sensing images based on deep learning[J]. Microelectronics & Computer, 2021, 38(1): 51-56.

针对遥感图像大棚提取的深度学习模型研究

基金项目: 

国家自然科学基金项目 61502357

国家自然科学基金项目 61502358

详细信息
    作者简介:

    宋灿  男,(1992-),硕士研究生.研究方向为深度学习和语义分割

    吴谨  女,(1967-),博士,教授.研究方向为图像处理与模式识别

    邓慧萍  女,(1983-),博士,副教授.研究方向为图像处理和多媒体通信

    通讯作者:

    朱磊(通讯作者)  男,(1982-),博士,副教授.研究方向为语义分割、目标检测、显著性检测等. E-mail:838994872@qq.com

  • 中图分类号: TP391

Greenhouse segmentation of remote sensing images based on deep learning

  • 摘要:

    大棚面积统计是蔬菜业进行科学管理的客观需求,通过深度学习自动获取大棚面积能更好地降低人工成本.为了准确获取大棚面积,本文使用基于深度学习的模型框架来解决这一问题.首先,使用金字塔池化模块和链式池化模块分别处理大尺度和小尺度的高层特征,在保持分辨率和避免退化的情况下提取多尺度的语义信息;然后,使用递归残差模块交替融合高层和低层信息,进一步优化分割结果。通过和三种较新的算法进行比较,实验结果表明本文的方法取得了最优的结果.

     

  • 图 1  遥感图像中大棚分割流程框架

    图 2  金字塔池化模块设计

    图 3  链式池化模块设计

    图 4  递归残差模块设计

    表  1  实验结果对比

    mIoU MAE Precision Recall F-Measure
    DeepLabv3 0.631 9 0.153 1 0.773 5 0.7262 0.762 0
    UNet 0.783 2 0.091 0 0.800 8 0.872 2 0.816 2
    PSPNet 0.796 9 0.056 6 0.840 6 0.818 3 0.835 3
    OUR 0.807 2 0.051 7 0.844 5 0.837 1 0.842 8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-14
  • 修回日期:  2020-05-09

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