Research on Semi Supervised Small Sample Tree Species Classification Based on Entropy Weight and Ensemble Learning
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.
Drone imageryentropy weight methoddeep learningensemble learningsemi-supervised small-sample classifica-tiontree species classificationtree species mappingEW-EL
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