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山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (3): 44-54.doi: 10.6040/j.issn.1672-3961.0.2023.001

• 机器学习与数据挖掘 • 上一篇    下一篇

基于多尺度特征模糊卷积神经网络的遥感图像分割

马翔悦1,徐金东1,倪梦莹2*   

  1. 1.烟台大学计算机与控制工程学院, 山东 烟台 264005;2.烟台大学物理与电子信息学院, 山东 烟台 264005
  • 发布日期:2024-06-28
  • 作者简介:马翔悦(1997— ),女,山东滨州人,硕士研究生,主要研究方向为图像分割. E-mail:maxiangyue_ytu@163.com. *通信作者简介:倪梦莹(1980— ),女,山东荣成人,讲师,硕士,主要研究方向为图像处理与模式识别. E-mail:nimengying@ytu.edu.cn
  • 基金资助:
    国家自然科学基金面上资助项目(62072391);国家自然科学基金地区科学基金资助项目(62066013)

Remote sensing image segmentation based on multi-scale feature fuzzy convolutional neural network

MA Xiangyue1, XU Jindong1, NI Mengying2*   

  1. 1. School of Computer and Control Engineering, Yantai University, Yantai 264005, Shandong, China;
    2. School of Physics and Electronic Information, Yantai University, Yantai 264005, Shandong, China
  • Published:2024-06-28

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摘要： 为解决高分辨率遥感图像“同谱异物、同物异谱”的不确定性以及大量空间信息利用率低的问题,提出一种基于多尺度特征的模糊卷积神经网络模型。该模型在长跳跃连接部分加入模糊学习模块去除噪声特征,缓解类别间的不确定性;利用多孔空间金字塔池化融合多尺度特征,提取完备的空间上下文信息,提升分割性能。试验结果表明,该模型在Potsdam数据集和Vaihingen数据集上的整体准确度分别达到92.65%和93.19%,明显优于现有流行的深度学习模型,能够显著提升高分辨率遥感图像的语义分割性能。

关键词: 模糊学习, 多孔空间金字塔池化, 多尺度特征, 编码器-解码器, 卷积神经网络

中图分类号: 

  • TP751
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