中国防汛抗旱2026，Vol.36Issue(2)：1-5,20,6.DOI:10.16867/j.issn.1673-9264.2025284
基于小样本学习的水体水利工程智能识别方法研究
Research on intelligent recognition methods for hydraulic engineering based on few-shot learning
摘要
Abstract
Currently,the identification of features in remote sensing images primarily relies on deep learning methods.However,these methods require large volumes of high-quality training samples and tend to perform poorly in tasks such as water conservancy project recognition,where training samples are scarce.To address this issue,introducing machine learning techniques.Based on the analysis and mining of historical project samples,newly collected samples are matched against historical ones to enable the identification and classification of new engineering projects.Using examples such as reservoirs and rivers,this method was applied for feature recognition.The results demonstrate that the approach can effectively identify features such as rivers,lakes,and reservoirs,with an accuracy exceeding 95%for rivers and over 81%for reservoirs.Moreover,it operates with high computational speed and delivers reliable recognition accuracy.关键词
机器学习/遥感影像/水利工程/地物识别/智能检测Key words
machine learning/remote sensing image/hydraulic engineering/ground object recognition/intelligent detection分类
天文与地球科学引用本文复制引用
刘媛媛,刘业森,俞茜,李敏,李匡,刘舒..基于小样本学习的水体水利工程智能识别方法研究[J].中国防汛抗旱,2026,36(2):1-5,20,6.基金项目
水利青年拔尖人才项目(JHQB202221). （JHQB202221）
