## 3 Experimental Results and Discussions
### RS Scene Few-Shot Classification Results and Analysis
The iterations utilize combinations of distortion parameters (D, P, C), starting from the smallest values. (**A,D**) correspond to the UCM dataset, (**B,E**) correspond to the AID dataset, and (**C,F**) correspond to the NWPU dataset. The different colored curves in the graph represent scenarios with 1 and 5 samples per class, respectively. The nodes indicate the maximum average accuracy value, corresponding to the optimal distortion magnitude. Figure 7 illustrates the variations in validation accuracy during the learning process of the dual-path classification network. It is evident that, in the training process of the five-way-five-shot scenario, the introduction of branch B significantly boosts the classification accuracy. Even in the five-way-one-shot training scenario, branch B provides performance gains. However, the five-shot curve demonstrates more pronounced gains compared to the one-shot scenario. Hence, in the context of few-shot classification, the greater the number of samples in the support set, the more pronounced the impact of ODS-DC on classification accuracy. Table 3 presents a performance comparison between the method proposed in this paper and currently advanced data augmentation-based few-shot learning methods. All the methods include data augmentation techniques such as random rotation, random cropping, and translation, and employ ResNet-12 as the backbone network for testing in a five-way scenario. It is evident that the method introduced in this paper outperforms other methods across the three widely used datasets. In addition, Table 4 illustrates the variation in classification accuracy when different backbone networks are employed as feature extractors. As observed from the table, using deeper feature extractors leads to significantly better classification performance. However, it is worth noting that backbone networks with more layers tend to be more complex and demand greater computational resources. For example, transitioning from Conv-4 to ResNet-12 increases the number of layers by threefold, resulting in substantial accuracy improvement. On the other hand, substituting ResNet-12 with ResNet-50, which increases the number of layers by more than fourfold, yields only a minor accuracy gain. Hence, for the method proposed in this paper, the choice of the backbone network is not solely based on having more layers but rather involves a comprehensive consideration of factors such as gains in accuracy, computational resource utilization, and the efficiency of method reproduction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**UCM (\%)**} & \multicolumn{2}{c}{**AID (\%)**} & \multicolumn{2}{c}{**NWPU (\%)**} \\ \cline{2-7}  & **1 Shot** & **5 Shot** & **1 Shot** & **5 Shot** & **1 Shot** & **5 Shot** \\ \hline ProtoNet [48] & 58.79 \(\pm\) 0.81 & 72.82 \(\pm\) 0.60 & 60.18 \(\pm\) 0.78 & 74.00 \(\pm\) 0.61 & 62.78 \(\pm\) 0.85 & 80.19 \(\pm\) 0.52 \\ MAML [49] & 54.97 \(\pm\) 0.69 & 65.45 \(\pm\) 0.70 & 56.50 \(\pm\) 0.65 & 70.02 \(\pm\) 0.50 & 56.01 \(\pm\) 0.87 & 72.94 \(\pm\) 0.63 \\ RelationNet [50] & 55.32 \(\pm\) 0.87 & 72.59 \(\pm\) 0.53 & 56.17 \(\pm\) 0.80 & 73.94 \(\pm\) 0.57 & 55.84 \(\pm\) 0.88 & 75.78 \(\pm\) 0.57 \\ RS-MetaNet [51] & 63.75 \(\pm\) 0.51 & 76.94 \(\pm\) 0.29 & 64.18 \(\pm\) 0.49 & 76.68 \(\pm\) 0.30 & 72.04 \(\pm\) 0.43 & 82.69 \(\pm\) 0.22 \\ SGMNet [52] & 64.17 \(\pm\) 0.75 & 76.63 \(\pm\) 0.59 & 64.32 \(\pm\) 0.79 & 77.98 \(\pm\) 0.42 & 73.01 \(\pm\) 0.77 & 84.52 \(\pm\) 0.50 \\ ODS-DC (ours) & 65.93 \(\pm\) 0.94 & 77.60 \(\pm\) 0.72 & 66.28 \(\pm\) 0.89 & 79.04 \(\pm\) 0.69 & 73.93 \(\pm\) 0.90 & 84.66 \(\pm\) 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between the dual-path classification method in this paper and the current benchmark methods. Figure 7: The variation curves of validation accuracy during the training process of the dual-path classification method (5-way).