Remote sensing image scene classification based on transfer learning and EfficientNetV2
In view of the low classification accuracy of traditional remote sensing image classification methods,this paper proposed a remote sensing image scene classification method based on transfer learning and an efficient scaled-down second generation of the neural network model(EfficientNetV2).Firstly,EfficientNetV2,which had fewer parameters and higher classification accuracy,was selected as the infrastructure.Secondly,the pre-trained network parameters were used to initialize the model through the migration learning strategy,which effectively avoided the overfitting phenomenon of the model.Finally,the experimental results on the aerial image dataset(AID)and the remote sensing image scene dataset(NWPU45)show that the classification accuracy of the method on these two datasets reaches 95.76%and 94.76%,respectively,fully proving the effectiveness and superiority of the proposed method.
国家科技期刊平台
NSTL
万方数据
