GLFFNet model for remote sensing image scene classification

被引：0

作者：

Wang W. ^{[1
]}

Deng J. ^{[1
]}

Wang X. ^{[1
]}

Li Z. ^{[2
]}

Yuan P. ^{[3
]}

机构：

[1] School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha

[2] Hunan Shenfan Science and Technology Limited Company, Changsha

[3] Changsha Jingwang Information and Technology Limited Company, Changsha

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2023年 / 52卷 / 10期

关键词：

convolutional neural network; GLFFNet model; remote sensing image; scene classification; Transformer;

D O I：

10.11947/j.AGCS.2023.20220286

中图分类号：

学科分类号：

摘要：

Traditional scene classification models cannot perform multi-scale key feature extraction in remote sensing images in a lightweight and efficient manner. Deep learning methods generally have shortcomings such as large amount of calculation and slow convergence speed. In view of the above problems, this paper makes full use of the ability of CNN structure and Transformer structure to extract features at different scales, and proposes a feature extract module, named global and local features fused (GLFF) block. Based on this module, a lightweight remote sensing image scene classification model, GLFFNet, is designed, which has better local information and global information extraction ability. In order to verify the effectiveness of GLFFNet, this paper uses the open-source remote sensing image datasets RSSCN7 and SIRI-WHU to verify the complexity and recognition ability of GLFFNet and other deep learning networks. Finally, GLFFNet achieves recognition accuracy of up to 94.82% and 95.83% on RSSCN7 and SIRI-WHU datasets, respectively, which is better than other state-of-the-art models. © 2023 SinoMaps Press. All rights reserved.

引用

页码：1693 / 1702

页数：9

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