KEYWORDS: Remote sensing, Image classification, Data modeling, Convolution, Education and training, Scene classification, Batch normalization, Matrices, Neural networks, Process modeling
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.
In order to improve the overall accuracy (OA) of the AlexNet model for high-resolution remote sensing scene images with complex backgrounds, we proposed an improved remote sensing scene image classification model. Firstly, we used Layer Normalization (LN) to replace the Local Response Normalization (LRN) in AlexNet and changed the convolution kernel of the first convolution layer to 7 × 7. Secondly, to focus on critical information in the feature extraction process, and suppress irrelevant background information, the two attention modules of Convolution Block Attention Module (CBAM) and Squeeze and Excitation Module (SEM) were combined. In this study, the classification verification was performed on three remote sensing scene datasets of NWPU-RESISC45, AID, and UCM, and achieved 96.29%, 96.02%, and 96.57% overall accuracy, respectively. Compared with AlexNet, the OA improved by 14.38%, 12.09%, and 9.9%, respectively, therefore, the improved model of this study can significantly distinguish between object information and background information in remote sensing scene imagery.
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