## 3 Results
### Experimental Setup  
In order to make full use of the dataset, it was necessary to preprocess the images. The images were randomly cropped to a size of 224 \(\times\) 224 and were preprocessed using horizontal flipping and other operations. In order to reasonably evaluate the experimental results, we followed the dataset split ratio used in recent papers. For AID, 20% and 50% of the images were randomly selected as the training set, and the rest were used as the test set. A total of 5% of the training set images were randomly selected as the validation set. For the NWPU-RESISC45 dataset, 10% and 20% of the images were randomly selected as the training set, and the rest were used as the test set. A total of 5% of the training set images were randomly selected as the validation set. The equation accuracy (OA) and confusion matrix were used to evaluate the performance of the network. The formula for calculating the overall accuracy is as follows:  
\[\text{OA}=\frac{1}{N}\sum_{i=1}^{r}x_{ii} \tag{9}\]  
In the experiments, the datasets were randomly divided into the training set, validation set, and test set to evaluate the performance of the proposed method. To avoid randomness, we repeated the experiment five times. The average classification results in the test set are reported in the Experimental Results section. The results are used to calculate the mean and variance. We used the PyTorch framework to build the network, used ImageNet pretrained weights for weight initialization and used stochastic gradient descent (SGD) combined with cosine annealing [38] to train the network. The learning rate calculation equation for cosine annealing is as follows:  
\[\eta_{t}=\eta_{min}+\frac{1}{2}(\eta_{max}-\eta_{min})\bigg{(}1+cos(\frac{T_{ curr}}{T_{max}}\pi)\bigg{)} \tag{10}\]  
Figure 6: Example images from AID. where \(\eta_{I}\) is the learning rate. \(\eta_{max}\) is the maximum learning rate, and here, \(\eta_{max}\) is set to 0.1. \(\eta_{min}\) is the minimum learning rate, and here, \(\eta_{min}\) is set to 0. \(T_{cur}\) is the current number of iterations, and \(T_{max}\) is the maximum number of iterations. We implemented our method using the ResNet50 and ECA modules pretrained on ImageNet images. The initial learning rate of the external attention module and ResNet50 with ECA modules in AEMMN was set to 0.005; the rest of the initial learning rate was set to 0.05; and the batch size was 48. The momentum of the SGD optimizer is set to 0.9, and the weight decay was set to \(5\times 10^{-4}\). We used a NVIDIA GeForce GTX 3080 GPU for training. In the experiment, the \(\alpha\) parameter of the gradient loss function in Equation (1) was 1, and the \(\beta\) parameter was 0.7. There were 100 hidden coarse-grained feature points that were generated by the ResNet50 with ECA module, and there were 500 hidden fine-grained feature points. The coarse-grained and fine-grained labels of the NWPU-RESISC45 and AID datasets used in this paper are listed in Tables 1 and 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Coarse-Grained Label** & **Fine-Grained Label** \\ \hline \multirow{4}{*}{Cultivated} & Circular farmland \\ \cline{2-3}  & Rectangular farmland \\ \cline{2-3}  & Terrace \\ \hline \multirow{4}{*}{Woodland} & Chaparral \\ \cline{2-3}  & Forest \\ \cline{2-3}  & Wetland \\ \hline Grassland & Meadow \\ \hline Commercial Service & Commercial area \\ \hline \multirow{4}{*}{Industrial and Mining} & Industrial area \\ \cline{2-3}  & Thermal power station \\ \hline \multirow{4}{*}{Residential} & Dense residential \\ \cline{2-3}  & Medium residential \\ \cline{2-3}  & Sparse residential \\ \hline \multirow{4}{*}{Public} & Baseball diamond \\ \cline{2-3}  & Basketball court \\ \cline{2-3}  & Golf course \\ \cline{2-3}  & Ground track field \\ \cline{2-3}  & Mobile home park \\ \cline{2-3}  & Runway \\ \cline{2-3}  & Tennis court \\ \cline{2-3}  & Stadium \\ \hline \multirow{4}{*}{Special} & Church \\ \cline{2-3}  & Palace \\ \cline{2-3}  & Storage tanks \\ \hline \hline \end{tabular}
\end{table}