改进U-Net的多尺度特征融合遥感图像语义分割网络  

Improved U-Net Multi-scale Feature Fusion Semantic Segmentation Network for Remote Sensing Images

作　　者：姜文文 夏英[1,2] JIANG Wenwen;XIA Ying(College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Key Laboratory of Tourism Multisource Data Perception and Decision Technology,Ministry of Culture and Tourism,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]旅游多源数据感知与决策技术文化和旅游部重点实验室,重庆400065

出　　处：《计算机科学》2025年第5期212-219,共8页Computer Science

基　　金：重庆市教委重点合作项目(HZ2021008);文化和旅游部重点实验室资助项目(E020H2023005)。

摘　　要：遥感图像的空间分辨率高,不同类型对象的尺度差异大、类别不平衡,是精准语义分割任务所面临的主要挑战。为了提高遥感图像语义分割的准确性,提出了一种改进U-Net的多尺度特征融合遥感图像语义分割网络(Multi-scale Feature Fusion Network,MFFNet)。该网络以U-Net网络为基础,包含动态特征融合模块和门控注意力卷积混合模块。其中,动态特征融合模块代替跳跃连接,改进上采样层和下采样层的特征融合方式,避免特征融合导致信息丢失,同时提高浅层特征和深层特征的融合效果;门控注意力卷积混合模块通过整合自注意力、卷积和门控机制,更好地捕获局部和全局信息。在Potsdam和Vaihingen数据集上开展对比实验和消融实验,结果表明MFFNet在两个数据集上的mIoU分别达到76.95%和72.93%,有效提高了遥感图像的语义分割精度。High spatial resolution of remote sensing images,the large scale differences of different types of objects,and the imba-lance of categories are the main challenges faced by accurate semantic segmentation tasks.In order to improve the accuracy of semantic segmentation of remote sensing images,this paper proposes an improved U-Net multi-scale feature fusion semantic segmentation network for remote sensing image(Multi-scale Feature Fusion Network,MFFNet).The network is based on the U-Net network and includes a dynamic feature fusion module and a gated attention convolution mix module.Among them,the dynamic feature fusion module replaces the skip connection and improves the feature fusion method of the upsampling layer and the downsampling layer to avoid information loss caused by feature fusion,while improving the fusion effect of shallow features and deep features.Gated attention convolution mix module integrates self-attention,convolution,and gating mechanisms to better capture both local and global information.Comparative experiments and ablation experiments are carried out on Potsdam and Vaihingen.The results show that the mIoU of MFFNet on the two datasets reached 76.95%and 72.93%respectively,effectively improving the semantic segmentation accuracy of remote sensing images.

关 键 词：语义分割 遥感图像 注意力机制 特征融合 门控机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]