计算机应用与软件2024，Vol.41Issue(8)：271-274,344,5.DOI:10.3969/j.issn.1000-386x.2024.08.039
基于改进的U-Net的遥感图像语义分割
SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGE BASED ON IMPROVED U-NET
陈松钰 1左强 1王志芳1
作者信息
- 1. 黑龙江大学电子工程学院 黑龙江哈尔滨 150080
- 折叠
摘要
Abstract
Semantic segmentation of remote sensing images is to classify each pixel in the image according to the type of land cover.It is an important research direction in the field of remote sensing image processing.The segmentation is inaccurate due to similar features in the research process.In order to solve this problem,DeepResU-Net,a remote sensing image semantic segmentation network based on U-Net and residual network is proposed.It improved the traditional U-Net semantic segmentation network,used U-Net as the skeleton network,and used residual convolution unit to replace the convolutional layer in the coding layer and decoding layer of the original U-Net,so as to prevent the network gradient from disappearing.The network contained rich jump connections that could promote information dissemination.Experiments on the remote sensing(ISPRS)Vaihingen dataset show that the results obtained by this method are more accurate than FCN-8s,SegNet,U-Net,and ResU-Net.关键词
语义分割/残差单元/U-NetKey words
Semantic segmentation/Residual unit/U-Net分类
计算机与自动化引用本文复制引用
陈松钰,左强,王志芳..基于改进的U-Net的遥感图像语义分割[J].计算机应用与软件,2024,41(8):271-274,344,5.
