吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (1): 248-254.doi: 10.13229/j.cnki.jdxbgxb20211389

• 计算机科学与技术 • 上一篇    

基于改进SegNet的遥感图像建筑物分割方法

朱冰1(),李紫薇2,李奇1,3()   

  1. 1.长春理工大学 计算机科学技术学院，长春 130022
    2.吉林师范大学 计算机学院，吉林 四平 136000
    3.长春理工大学 中山研究院，广东 中山 528437
  • 收稿日期:2021-12-16 出版日期:2023-01-01 发布日期:2023-07-23
  • 通讯作者: 李奇 E-mail:baulina@163.com;liqi@cust.edu.cn
  • 作者简介:朱冰（1992-），女，博士研究生. 研究方向：仿脑智能与脑信息学. E-mail： baulina@163.com
  • 基金资助:
    国家自然科学基金青年基金项目(81600923);深圳海外创新团队项目(KQTD20180413181834876);吉林省自然科学基金项目(20210101273JC);吉林省卫生与健康技术创新项目(2020J052);吉林大学白求恩计划项目(2020B47)

Building segmentation method of remote sensing image based on improved SegNet

Bing ZHU1(),Zi-wei LI2,Qi LI1,3()   

  1. 1.College of Computer Science and Technology，Changchun University of Science and Technology，Changchun 130022，China
    2.College of Computer，Jilin Normal University，Siping 136000，China
    3.Zhongshan Institute，Changchun University of Science and Technology，Zhongshan 528437，China
  • Received:2021-12-16 Online:2023-01-01 Published:2023-07-23
  • Contact: Qi LI E-mail:baulina@163.com;liqi@cust.edu.cn

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摘要：

针对高分辨率遥感图像建筑物分割精度低以及边缘模糊问题，在SegNet网络的基础上提出一种改进的全卷积神经网络。首先，选择在深度学习任务中表现良好的GELU作为激活函数，避免神经元失活；其次，在编码网络中使用改进的残差瓶颈结构提取更多的建筑物特征；然后，利用跳跃连接融合图像的低级与高级语义特征，辅助图像重构；最后，在解码网络末端连接改进的边缘修正模块进一步修正建筑物边缘细节，提升建筑物的边缘完整度。在Massachusetts Buildings Dataset数据集上进行实验，其精确率、召回率和F1值分别达到93.5%、79.3%和81.9%，综合评价指标F1值相比于基础网络提升约5%。

关键词: 计算机应用技术, 遥感图像, 建筑物分割, SegNet, 全卷积神经网络

Abstract:

Aiming at the low accuracy of building segmentation and boundary blur in high resolution remote sensing images， an improved full convolution network is proposed based on SegNet network. Firstly， GELU， which performs well in deep learning task， is selected as the activation function to avoid neuron inactivation； Secondly， the improved residual bottleneck structure is used to extract more building features in the encoding network； Then， the skip connection is used to fuse the low-level and high-level semantic features of the image to assist image reconstruction； Finally， an improved boundary refinement module is connected at the end of the decoding network to further correct the boundary details of the building and improve the boundary integrity of the building. The experiment is carried out on the Massachusetts Buildings Dataset. The accuracy， recall and F1 score reach 93.5%， 79.3% and 81.9% respectively. The F1 score of the comprehensive evaluation index is about 5% higher than that of the basic network.

Key words: computer application technology, remote sensing images, building segmentation, SegNet, full convolution network

中图分类号: 

  • TP389.1

图1

RsBR-SegNet模型结构图"

图2

GELU激活函数图像"

图3

改进的残差瓶颈结构图"

图4

边缘修正模块对比图"

图5

Satellite dataset I （global cities）分割结果对比图"

表1

Satellite dataset I （global cities）评价"

网络名称精确率召回率F1值交并比
FCN0.863 150.692 520.725 430.575 86
SegNet0.839 970.620 440.666 170.506 69
U?Net0.866 160.731 940.731 960.598 67
RsBR?SegNet0.875 260.754 580.759 070.618 34

图6

Massachusetts Buildings Dataset分割结果对比图"

表2

Massachusetts Buildings Dataset评价"

网络名称精确率召回率F1值交并比
FCN0.917 890.738 960.771 180.630 75
SegNet0.918 440.731 450.770 000.629 98
U?Net0.923 450.758 020.793 030.674 29
RsBR?SegNet0.935 030.792 880.819 150.697 46
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