北京测绘2024，Vol.38Issue(11)：1521-1525,5.DOI:10.19580/j.cnki.1007-3000.2024.11.002
基于迁移学习和EfficientNetV2的遥感图像场景分类
Remote sensing image scene classification based on transfer learning and EfficientNetV2
摘要
Abstract
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.关键词
遥感图像/场景分类/EfficientNetV2/注意力机制,迁移学习Key words
remote sensing image/scene classification/EfficientNetV2/attention mechanism/transfer learning分类
天文与地球科学引用本文复制引用
梁杰文..基于迁移学习和EfficientNetV2的遥感图像场景分类[J].北京测绘,2024,38(11):1521-1525,5.基金项目
广东省科技计划(2021B1212100003) （2021B1212100003）
