森林工程2025，Vol.41Issue(1)：151-161,11.DOI:10.7525/j.issn.1006-8023.2025.01.012
基于熵权与集成学习的半监督小样本树种分类研究
Research on Semi Supervised Small Sample Tree Species Classification Based on Entropy Weight and Ensemble Learning
摘要
Abstract
To address the issue that traditional semi-supervised self-training classification methods can lead to dataset confusion,affecting the accuracy of subsequent small-sample tree species classification,an EW-EL(entropy weight and ensemble learning)semi-supervised small-sample tree species classification method is proposed based on the entropy weight method(EW)and ensemble learn-ing(EL).EW-EL introduces the concept of EL into the theoretical framework of traditional semi-supervised self-training classification methods,using the entropy weight method as a foundational theory.It calculates the information entropy based on the F1 score of base classifiers in the current training cycle as a weight factor.Then,design the weights according to the idea that the larger the information entropy,the more unstabel the base classifier will be.This will make the classification probabilities of the ensemble classifier more concentrated and reduce the bias of the ensemble classifier.The findings demonstrate that,in contrast to conventional semi-super-vised self-training techniques,EW-EL can efficiently balance data distribution,producing more precise pseudo-label sample catego-ries for recently added data.With a recall of 0.96 and a Kappa coefficient of 0.97,the overall accuracy(OA)of the EW-EL method for small-sample tree species classification is 0.97.All three indicators are superior to supervised classification,conventional semi-su-pervised self-training techniques,and semi-supervised self-training techniques built using conventional EL mechanisms.In particu-lar,the EW-EL approach outperforms semi-supervised self-training techniques that incorporate a soft voting mechanism in terms of OA and recall by 1%.Furthermore,in the chosen test area,the tree species map produced with EW-EL in combination with basic linear iterative clustering reached 94%accuracy.Moreover,extra analyses show that EW-EL can integrate several classifiers to provide bet-ter small-sample tree species classification results,which makes it more appropriate for relevant departments in forestry resource statis-tics under low-cost circumstances.关键词
无人机影像/熵权法/深度学习/集成学习/半监督小样本分类/树种分类/树种制图/EW-ELKey words
Drone imagery/entropy weight method/deep learning/ensemble learning/semi-supervised small-sample classifica-tion/tree species classification/tree species mapping/EW-EL分类
计算机与自动化引用本文复制引用
王静,李静..基于熵权与集成学习的半监督小样本树种分类研究[J].森林工程,2025,41(1):151-161,11.基金项目
国家自然科学基金项目(62071171) （62071171）
黄炎培职业教育思想研究规划课题(ZJS2022Zd33) （ZJS2022Zd33）
中西部地区本科层次职业教育理论与实践研究(22GDZY0229) （22GDZY0229）
面向复杂场景的小目标检测与识别关键技术研究(242102210071). （242102210071）
