## 3 Results
### Experimental Results  
Table 3 shows the experimental results of some of the methods used on the NWPU-RESISC45 dataset, where the training set proportions were 10% and 20%. When the training set ratio was 10%, the overall accuracy of the proposed method was 92.07%, which is 3.59% higher than the original backbone network ResNet50 and 0.42% higher than BMDF-LCNN, achieving the highest accuracy among the methods in the table. When the training set ratio was 20%, it was 2.67% higher than the original backbone network ResNet50 and 0.96% higher than BMDF-LCNN, demonstrating the highest accuracy among the methods in the table. Figures 7 and 8 show the confusion matrix of the proposed method on the NWPURESISC45 dataset with training set ratios of 10% and 20%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Coarse-Grained Label** & **Fine-Grained Label** \\ \hline \multirow{4}{*}{Special} & Church \\ \cline{2-3}  & Resort \\ \cline{2-3}  & Storage tanks \\ \hline \multirow{4}{*}{Transportation land} & Airport \\ \cline{2-3}  & Bridge \\ \cline{2-3}  & Parking \\ \cline{2-3}  & Port \\ \cline{2-3}  & Railway station \\ \cline{2-3}  & Viaduct \\ \hline \multirow{4}{*}{Water} & Beach \\ \cline{2-3}  & Pond \\ \cline{2-3}  & River \\ \hline \multirow{4}{*}{Other} & Bare land \\ \cline{2-3}  & Ober \\ \cline{2-3}  & Desert \\ \cline{2-3}  & Mountain \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Overall Accuracy**} \\ \cline{2-3}  & **10\% Training Ratio** & **20\% Training Ratio** \\ \hline (Fine-tuning) GoogleNet [39] & 82.57 \(\pm\) 0.14 & 86.02 \(\pm\) 0.18 \\ (Fine-tuning) VGG-16 [40] & 87.15 \(\pm\) 0.45 & 90.36 \(\pm\) 0.18 \\ (Fine-tuning) ResNet50 [16] & 88.48 \(\pm\) 0.21 & 91.86 \(\pm\) 0.19 \\ D-CNN [41] & 89.22 \(\pm\) 0.50 & 91.89 \(\pm\) 0.22 \\ MG-CAP [42] & 90.83 \(\pm\) 0.12 & 92.85 \(\pm\) 0.11 \\ BMDF-LCNN [43] & 91.65 \(\pm\) 0.15 & 93.5 \(\pm\) 70.22 \\ SCCov [44] & 89.30 \(\pm\) 0.35 & 92.10 \(\pm\) 0.25 \\ MLFF [45] & 90.01 \(\pm\) 0.33 & 92.45 \(\pm\) 0.20 \\ T-CNN [46] & 90.25 \(\pm\) 0.14 & 93.05 \(\pm\) 0.12 \\ Ours & 92.07 \(\pm\) 0.14 & 94.53 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall accuracy (%) of different methods with training ratios of 50% and 20% in the NWPU-RESISC45 dataset. Figure 8: Confusion matrix of the proposed method on the NWPU-RESISC45 dataset with a training ratio of 20%. The content of the matrix is the accuracy of the predicted category within each category. Figure 7: Confusion matrix of the proposed method on the NWPU-RESISC45 dataset with a training ratio of 10%. The content of the matrix is the accuracy of the predicted category within each category. With a training ratio of 10%, although no scene class had 100% accuracy, most classes demonstrated an accuracy higher than 90%. Some scenes, such as those of churches, had low accuracy, as seen from the confusion matrix, and this could be because the proposed method confuses it with the palace and the commercial area labels. For the training ratio of 20%, the accuracy of the cloud, chaparral, and iceberg categories was 100%; the accuracy of most of the categories was above 95%, with the exception of some categories such as those for churches and railway stations. Some scenes, such as churches and palaces are confused with one another due to the similar building shapes, resulting in low accuracy. Table 4 shows the comparison of experimental results of some of the methods on the AID when the training set ratios were set to 20% and 50%. When the training ratio was 20%, the overall accuracy of the proposed method reached 94.96%, which is 0.41% higher than the T-CNN method, demonstrating the highest accuracy among the latest methods in the table, and 2.39% higher than the original backbone network ResNet50. When the training set ratio was 50%, the overall accuracy of the proposed method was 96.72%, which is 0.76% higher than the original backbone network ResNet50.