Embedding BN layers into AlexNet for remote sensing scene image classification

Dongfu Dai; Weiheng Xu; Shaodong Huang

doi:10.1117/12.2680154

27 June 2023 Embedding BN layers into AlexNet for remote sensing scene image classification

Dongfu Dai, Weiheng Xu, Shaodong Huang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270513 (2023) https://doi.org/10.1117/12.2680154
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

To improve model convergence speed and accuracy of the AlexNet model on high-resolution remote sensing scene image classification, the Batch Normalization (BN) layer were used to replace the Local Response Normalization (LRN) to normalize the features of each channel in the convolution layers. In addition, we replaced the filling method of all layers in the AlexNet model with the "SAME" method to reduce the loss of image edge information in convolution. Moreover, we add dropout strategy after each pooling layer to prevent model overfitting. Finally, three remote sensing scene datasets including NWPU-RESISC45, AID, and UCM were used for accuracy and convergence speed verification. The overall accuracies(OA) of our improved model were 96.10%, 96.80%, and 97.14% of on the three datasets, respectively, which were 14.19%, 13.68%, and 10.47% higher than those of AlexNet, respectively. Meanwhile, compare with other models, this study model has higher OA for remote sensing scene image classification. Therefore, the improved model of this study can accurately identify scene categories.

Citation Download Citation

Dongfu Dai, Weiheng Xu, and Shaodong Huang "Embedding BN layers into AlexNet for remote sensing scene image classification", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270513 (27 June 2023); https://doi.org/10.1117/12.2680154

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