## 4 Experiments
### Comparison with State-of-the-Art Methods  
In this section, the performance achieved with the proposed GFSC is compared with that of several other state-of-the-art methods. To ensure fair comparisons, several methods were chosen, including DCNN [16], EAM [14], MGCAP [56], FACNN [17], CNN-CapsNet [57], DDRL-AM [58], and HABFNet [5]. Most state-of-the-art methods use ImageNet-pretrained CNNs to generate the scene representations for RSSC. It should be emphasized that many of these models are carefully designed, and many are based on a feature fusion approach; in contrast, our approach uses only common CNNs. Table 4 shows the results obtained on three RSSC datasets. The proposed GFSC achieved accuracies of 94.82%, 97.56%, and 99.46% on the NWPU, AID, and UC-Merced datasets, respectively. In addition to high accuracy, we can see that our method also exhibits small standard deviations, indicating that it is more stable and robust than the other methods. Better results may be achieved if the feature fusion methods are used on the basis of our CNN backbones. The NWPU-RESISC45 dataset is still a challenging dataset, on which the performance achieved has not yet been saturated. Even for this dataset, WRN-50+GFSC achieved remarkable performance, with overall accuracies of 93.13% and 94.82% for training ratios of 10% and 20%, respectively. We show an example of the category-level classification results in Figure 7.  
For the experiments using the AID dataset, the training ratios were set to 20% and 50%. The performance comparisons between our method and the other state-of-the-art methods are shown in Table 4. Our proposed framework achieved consistent improvements with both training ratios. The improvement is more obvious when the amount of data for training is relatively small. Compared with the previous approaches, GFSC enables considerable improvements using only common backbone networks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CNN-Based Methods**} & \multicolumn{2}{c}{**NWPU-RESISC45**} & \multicolumn{2}{c}{**AID**} & \multicolumn{2}{c}{**UC-Merced**} \\ \cline{2-7}  & **T.R. = 10\%** & **T.R. = 20\%** & **T.R. = 20\%** & **T.R. = 50\%** & **T.R. = 50\%** & **T.R. = 80\%** \\ \hline DCNN [16] & 89.22 \(\pm\) 0.50 & 91.89 \(\pm\) 0.22 & 90.82 \(\pm\) 0.16 & 96.89 \(\pm\) 0.10 & — & 98.93 \(\pm\) 0.10 \\ MG-CAP (Bilinear) [56] & 89.42 \(\pm\) 0.19 & 91.72 \(\pm\) 0.16 & 92.11 \(\pm\) 0.15 & 95.14 \(\pm\) 0.12 & — & 98.60 \(\pm\) 0.26 \\ MG-CAP (Sqrt-E) [56] & 90.83 \(\pm\) 0.12 & 92.95 \(\pm\) 0.13 & 93.34 \(\pm\) 0.18 & 96.12 \(\pm\) 0.12 & — & 99.00 \(\pm\) 0.10 \\ CNN-CapsNet [57] & 89.03 \(\pm\) 0.21 & 92.60 \(\pm\) 0.11 & 93.79 \(\pm\) 0.13 & 96.63 \(\pm\) 0.12 & 97.59 \(\pm\) 0.16 & 99.05 \(\pm\) 0.24 \\ FACNN [17] & — & — & — & 95.15 \(\pm\) 0.11 & — & 98.81 \(\pm\) 0.24 \\ DDRL-AM [58] & 92.17 \(\pm\) 0.08 & 92.46 \(\pm\) 0.09 & 92.36 \(\pm\) 0.10 & 96.25 \(\pm\) 0.05 & — & 99.05 \(\pm\) 0.08 \\ ResNet-50+EAM [14] & 90.87 \(\pm\) 0.15 & 93.51 \(\pm\) 0.12 & 93.64 \(\pm\) 0.25 & 96.62 \(\pm\) 0.13 & — & 96.62 \(\pm\) 0.13 \\ ResNet-101+EAM [14] & 91.91 \(\pm\) 0.22 & 94.29 \(\pm\) 0.09 & 94.26 \(\pm\) 0.11 & 97.06 \(\pm\) 0.19 & — & 99.21 \(\pm\) 0.19 \\ HABFNet [5] & 92.75 \(\pm\) 0.18 & 94.54 \(\pm\) 0.06 & 95.48 \(\pm\) 0.26 & 96.95 \(\pm\) 0.17 & 98.47 \(\pm\) 0.47 & 99.29 \(\pm\) 0.35 \\ \hline
**ResNet-50 (ours)** & 92.08 \(\pm\) 0.17 & 94.16 \(\pm\) 0.08 & 95.59 \(\pm\) 0.22 & 97.02 \(\pm\) 0.11 & 98.06 \(\pm\) 0.32 & 99.03 \(\pm\) 0.27 \\
**ResNeXt-50 (ours)** & 92.94 \(\pm\) 0.13 & 94.73 \(\pm\) 0.10 & 95.97 \(\pm\) 0.15 & 97.39 \(\pm\) 0.12 & 98.39 \(\pm\) 0.29 & 99.22 \(\pm\) 0.16 \\
**WRN-50 (ours)** & 93.13 \(\pm\) 0.08 & 94.82 \(\pm\) 0.05 & 96.37 \(\pm\) 0.13 & 97.56 \(\pm\) 0.09 & 98.57 \(\pm\) 0.23 & 99.46 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the classification accuracies (%) achieved with our GFSC framework, CNN-based baselines, and state-of-the-art methods.