基于改进U-Net的遥感图像道路提取  

Road Extraction from Remote Sensing Image Based on an Improved U-Net

作　　者：何哲 陶于祥 罗小波[1,2] 徐浩[1,2] He Zhe;Tao Yuxiang;Luo Xiaobo;Xu Hao(School of Computer Sciences and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Spatial Big Data Research Center,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065 [2]重庆邮电大学空间大数据研究中心,重庆400065

出　　处：《激光与光电子学进展》2023年第16期377-384,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(41871226);国家重点研发计划政府间国际科技创新合作(2021YFE0194700);重庆市教委重点合作项目(HZ2021008);重庆市高技术产业重大产业技术研发项目(D2018-82)。

摘　　要：从遥感图像中提取的道路信息在城市规划、交通管理等领域具有极其巨大的价值,但由于背景复杂、障碍物遮挡以及大量类似的非道路区域的干扰,目前从遥感图像中提取高质量道路信息仍受到限制。为此,提出一种基于混合尺度注意力U-Net的遥感图像道路提取方法HSA-UNet。首先,在编码网络使用由残差结构和注意力特征融合机制构成的注意力残差学习单元,提高对全局特征和局部特征的提取能力;其次,由于道路具有跨度大、狭窄且连续分布等特点,在桥接网络加入注意力增强的空洞空间金字塔池化模块来增强不同尺度道路特征提取能力。在Massachusetts道路数据集上进行的实验结果表明,在F1、交并比等评价指标上,HSA-UNet明显优于D-LinkNet、DeepLabV3+等语义分割网络。Road information extracted from remote sensing images is of great value in urban planning,traffic management,and other fields.However,owing to the complex background,obstacles,and numerous similar nonroad areas,high-quality road information extraction from remote sensing images is still challenging.In this work,we propose HSA-UNet,a road information extraction method based on mixed-scale attention and U-Net,for high-quality remote sensing images.First,an attention residual learning unit,composed of a residual structure and an attention feature fusion mechanism,is used in the coding network to improve the extraction ability of global and local features.Second,owing to roads with the characteristics of large spans,narrowness,and continuous distribution,the attention-enhanced atrous spatial pyramid pooling module is added to the bridge network to enhance the ability of road features extraction at different scales.Experiments were performed on Massachusetts roads dataset,and the results showed that HSA-UNet significantly outperformed D-LinkNet,DeepLabV3+,and other semantic segmentation networks in terms of F1,intersection over union,and other evaluation indicators.

关 键 词：遥感 遥感图像 语义分割 道路提取 尺度注意力 注意力特征融合 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]