计算机工程与应用2024，Vol.60Issue(11)：194-203,10.DOI:10.3778/j.issn.1002-8331.2310-0302
基于改进Swin Transformer的遥感图像语义分割方法
Semantic Segmentation Method for Remote Sensing Images Based on Improved Swin Transformer
摘要
Abstract
Extracting accurate feature information in high-resolution remote sensing images plays an important role in urban planning as well as land resource utilization.However,remote sensing images are characterized by large scale dif-ferences between target objects and complex backgrounds,which easily lead to inaccurate extraction results,especially the low extraction accuracy for small-scale features.In order to solve these problems,this paper proposes a novel dual-coding structure to fully acquire global semantic information as well as spatial detail information,and to fuse feature infor-mation at different scales in stages to enhance the feature representation capability.The feature enhancement module(FEM)is constructed to reduce the loss of detail information in downsampling and focus on more small-scale features.In order to better refine the feature information,channel attention and kernel attention are fused and then up-sampled,which is able to integrate the local features with the corresponding global spatial dependencies and enhance the segmentation accuracy of the target object.The mIoU on Potsdam dataset and Vaihingen dataset are 86.1%and 82.4%,respectively.Comparative analysis with popular semantic segmentation models shows that the method in this paper can effectively solve the problem of inaccurate segmentation of small-and medium-scale objects in remote sensing images,and it is suit-able for dealing with the task of semantic segmentation of remote sensing images.关键词
语义分割/双编码结构/特征加强/融合注意力机制/小尺度地物Key words
semantic segmentation/dual coding structure/feature enhancement/fusion attention mechanism/small scale features分类
信息技术与安全科学引用本文复制引用
王一中,胡亚琦,吴小所,闫浩文,王小成..基于改进Swin Transformer的遥感图像语义分割方法[J].计算机工程与应用,2024,60(11):194-203,10.基金项目
国家重点研发计划(2022YFB3903604) （2022YFB3903604）
甘肃省自然科学基金(21JR7RA310) （21JR7RA310）
兰州交通大学青年科学基金(2021029). （2021029）
