## 3 Experimental Results and Discussions
### RS Scene Few-Shot Classification Results and Analysis  
Figure 6 illustrates the change in accuracy during the distortion magnitude search process. A maximum accuracy value was recorded after every 200 attempts. If this accuracy was higher than the previously recorded maximum, it was updated as the current accuracy; otherwise, it remained unchanged. The solid line represents the mean accuracy obtained from 100 complete search processes, each utilizing different support sets randomly extracted from the test set. The shaded region shows the range in which the 100 search operations' curves appeared. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Image Size** & \begin{tabular}{c} **Number of** \\ **Scenes** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & \
\begin{tabular}{c} **Samples per** \\ **Class** \\ \end{tabular} &
\begin{tabular}{c} **Resolution** \\ **(m)** \\ \end{tabular} \\ \hline UCM & 256 \(\times\) 256 & 21 & 2100 & 100 & 0.3 \\ AID & 600 \(\times\) 600 & 30 & 10,000 & 220–420 & 0.5–0.8 \\ NWPU & 256 \(\times\) 256 & 45 & 31,500 & 700 & 0.2–30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of experimental datasets. In the figures, we can observe that feature-based data augmentation significantly impacts the classifier's performance. Simultaneously, the segmented search approach substantially shortens the search process. Regardless of the different support sets used, the proposed method in this paper consistently identifies the optimal distortion magnitudes. Table 2 displays a comparison of the performance of the ODS method with advanced data augmentation models. It is evident that the proposed approach effectively leverages discriminative features, thereby enhancing the model's generalization performance. \begin{table}
\begin{tabular}{c 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 Mixup [33] & 49.51 \(\pm\) 1.51 & 65.11 \(\pm\) 1.26 & 49.99 \(\pm\) 1.43 & 66.54 \(\pm\) 1.30 & 58.03 \(\pm\) 1.96 & 74.39 \(\pm\) 1.77 \\ CutMix [44] & 52.69 \(\pm\) 1.80 & 67.12 \(\pm\) 1.45 & 53.52 \(\pm\) 1.67 & 68.37 \(\pm\) 1.59 & 61.64 \(\pm\) 2.31 & 75.69 \(\pm\) 2.01 \\ DAGAN [24] & 52.12 \(\pm\) 1.16 & 66.59 \(\pm\) 0.76 & 52.88 \(\pm\) 1.15 & 66.97 \(\pm\) 0.80 & 59.98 \(\pm\) 1.60 & 75.22 \(\pm\) 1.24 \\ f-DAGAN [26] & 53.25 \(\pm\) 0.44 & 67.31 \(\pm\) 0.35 & 55.89 \(\pm\) 0.41 & 68.10 \(\pm\) 0.33 & 63.86 \(\pm\) 0.87 & 76.28 \(\pm\) 0.64 \\ AugGAN [45] & 52.54 \(\pm\) 0.53 & 66.76 \(\pm\) 0.31 & 53.59 \(\pm\) 0.64 & 67.80 \(\pm\) 0.52 & 64.00 \(\pm\) 0.89 & 76.05 \(\pm\) 0.70 \\ Style Aug. [46] & 54.00 \(\pm\) 1.33 & 68.33 \(\pm\) 0.98 & 55.20 \(\pm\) 1.23 & 69.05 \(\pm\) 1.00 & 65.56 \(\pm\) 1.52 & 77.23 \(\pm\) 1.31 \\ AutoAug. [36] & 57.67 \(\pm\) 0.65 & 68.89 \(\pm\) 0.54 & 59.58 \(\pm\) 0.57 & 70.99 \(\pm\) 0.50 & 66.10 \(\pm\) 0.82 & 77.80 \(\pm\) 0.65 \\ RandAug. [32] & 58.76 \(\pm\) 0.95 & 70.85 \(\pm\) 0.62 & 60.09 \(\pm\) 1.19 & 72.74 \(\pm\) 0.90 & 67.94 \(\pm\) 0.99 & 79.64 \(\pm\) 0.67 \\ MADAO [47] & 59.40 \(\pm\) 0.73 & 71.31 \(\pm\) 0.56 & 61.06 \(\pm\) 0.70 & 72.60 \(\pm\) 0.47 & 66.87 \(\pm\) 0.96 & 79.96 \(\pm\) 0.58 \\ ODS (Ours) & 60.35 \(\pm\) 1.02 & 72.67 \(\pm\) 0.73 & 61.79 \(\pm\) 1.26 & 74.31 \(\pm\) 0.76 & 67.47 \(\pm\) 1.17 & 80.59 \(\pm\) 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the ODS method with advanced data augmentation methods in terms of classification accuracy on UCM, AID, and NWPU datasets (all using ResNet-12 as the backbone network). Figure 6: The mean accuracy update curves during the distortion magnitude search process (5-way). The vertical axis represents the accuracy of few-shot classification, and the horizontal axis represents the number of iterations.