西南林业大学学报2025，Vol.45Issue(9)：147-154,8.DOI:10.11929/j.swfu.202408024
基于深度学习的无标签超分辨率土地覆盖制图研究
Research on Label-free Super-resolution Land Cover Mapping Based on Deep Learning
摘要
Abstract
In this study,we propose a deep learning-based unlabeled land cover mapping method to achieve 2 m resolution land cover mapping in Guangze County,Fujian Province,without local high-resolution labels by integrating label super-resolution(SR)and Instance Batch Normalized Network(IBN-Net).The results show that label super-resolution can be achieved using the improved fully convolutional neural network(FCN)model,which upgrades the low-resolution labels to high-resolution and effectively improves the classification accuracy.The IBN-Net network enhances the generalization ability of the model and significantly improves the effectiveness of cross-domain applications.Compared with endogenous low-resolution labels,using high-precision exogenous la-bels improved the overall accuracy of the model in Glossy County by 2.55％to 85.48％.The method in this study significantly improves the accuracy of land cover mapping without matching labels,which can provide effective technical support for regional ecological monitoring and management.关键词
土地覆盖制图/标签超分辨率/深度学习/FCN网络/IBN网络Key words
land cover mapping/label super-resolution/deep learning/fully convolutional neural network/instance batch normalization network分类
天文与地球科学引用本文复制引用
汤媛媛,严恩萍,唐玉宾,聂小力,聂平静,亓梦茹..基于深度学习的无标签超分辨率土地覆盖制图研究[J].西南林业大学学报,2025,45(9):147-154,8.基金项目
武夷山重点地区生态修复综合调查项目(DD20230479)资助 （DD20230479）
南平市典型红壤区地表基质调查项目(DD20220865)资助 （DD20220865）
高植被密度区隐匿目标植被-土壤特征光谱指数分类与识别机理研究项目(KC20220013)资助 （KC20220013）
洞庭湖湿地生态修复综合调查项目(DD20230478)资助 （DD20230478）
东部平原湖区南部湖泊调查项目(DD20230506)资助 （DD20230506）
大别山区东段生态修复综合调查项目(DD20242416)资助. （DD20242416）
