计算机与数字工程2024，Vol.52Issue(5)：1495-1501,7.DOI:10.3969/j.issn.1672-9722.2024.05.041
基于改进U-Net模型的遥感影像道路提取方法研究
Research on Road Extraction Method from Remote Sensing Image Based on Improved U-Net Model
摘要
Abstract
In view of high-resolution remote sensing images being with complex background information,road extraction in the high-resolution remote sensing images being difficult and having a low degree of automation,this paper proposes an improved U-Net road extraction method.First,the VGG16 network structure is employed in the encoder to replace the original U-Net encoder structure,then,a feature compression activation module(SENet)is added after each encoder and decoder block to enhances the ability of network feature learning.Finally,the loss function combined with the Dice loss function and the binary cross-entropy loss function is used for training,which reduces the sample imbalance problem in the road extraction task.The experimental results on the Massachusetts Road data set show that the improved algorithm has effectively improved the road extraction results.The preci-sion,recall,F1-score and mIoU evaluation indicators of the proposed method on the test set reached 82.5%,77.8%,80.0%,and 82.1%,respectively.In the test image,it has a better recognition effect on roads with different widths and irregular shapes.关键词
U-Net/遥感影像/道路提取/特征压缩激活模块/复合损失函数Key words
U-Net/remote sensing images/road extraction/SENet/loss function分类
信息技术与安全科学引用本文复制引用
佟喜峰,张婉莹..基于改进U-Net模型的遥感影像道路提取方法研究[J].计算机与数字工程,2024,52(5):1495-1501,7.基金项目
黑龙江省自然科学基金项目(编号:LH2021F004) （编号:LH2021F004）
东北石油大学研究生教育创新工程项目(编号:JYCX_11_2020)资助. （编号:JYCX_11_2020）
