计算机工程与应用2025，Vol.61Issue(24)：86-102,17.DOI:10.3778/j.issn.1002-8331.2504-0043
遥感图像半监督语义分割方法研究综述
Survey on Semi-Supervised Semantic Segmentation Methods for Remote Sensing Images
摘要
Abstract
As an important research direction in the field of remote sensing technology,semantic segmentation of remote sensing images aims to segment remote sensing images at the pixel level and accurately classify them into predefined ground object categories.Traditional methods rely on a large number of pixel-level annotation data,but manual annotation is time-consuming and laborious.In order to solve this problem,a semi-supervised learning method is introduced.By com-bining a small number of labeled samples and a large number of unlabeled data for model training,the labeling require-ments are significantly reduced.By analyzing and summarizing the related research on semi-supervised semantic segmen-tation of remote sensing images in recent years,the classification system of existing methods is systematically sorted out,and its advantages and disadvantages are deeply analyzed.Starting from the core ideas and technical strategies,the existing semi-supervised semantic segmentation methods of remote sensing images are systematically summarized and classified,and their innovations and limitations are discussed respectively.The datasets widely used in the research of semi-super-vised semantic segmentation of remote sensing images are introduced.Based on the commonly used experimental settings and evaluation indicators,multi-method comparative analysis is carried out on different datasets.Finally,the future research trends of semi-supervised semantic segmentation of remote sensing images are discussed.关键词
遥感图像/语义分割/深度学习/半监督学习Key words
remote sensing image/semantic segmentation/deep learning/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
DONG Yifan,SUN Wenli,ZHAO Yang,HUANG Pingping..遥感图像半监督语义分割方法研究综述[J].计算机工程与应用,2025,61(24):86-102,17.基金项目
内蒙古自治区自然科学基金(2024MS06030,2024QN06016) （2024MS06030,2024QN06016）
内蒙古自治区直属高校基本科研业务费项目(JY20230008). （JY20230008）
