山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (6): 139-145.doi: 10.6040/j.issn.1672-3961.0.2021.328
李旭涛1,2,杨寒玉1,卢业飞1,张玮1,3*
LI Xutao1,2, YANG Hanyu1, LU Yefei1, ZHANG Wei1,3*
摘要： 为在遥感图像中提取出来道路信息,利用深度学习技术,引入U2-Net模型进行遥感图像道路分割。相比于传统的道路提取方法,基于U2-Net方法可以实现道路的自动化提取。为验证U2-Net模型分割效果,选取U-Net、DeepLabV3+等近几年较为流行的语义分割方法进行对比试验,并进一步分析U2-Net显著图融合模块中卷积核对道路提取效果的影响。试验结果表明,U2-Net模型能较有效地提取出道路信息,模型在测试集上的平均交并比达到了76.49%,Kappa达到了0.701 2,分割精度优于U-Net、DeepLabV3+等语义分割方法。基于U2-Net模型的深度学习方法可以用于解决遥感图像中的道路分割问题,并具有较好的分割效果。
中图分类号:
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