## 3 Experimental Results and Discussions
### Ablation Study  
We assessed the performance of ODS-DC under different combinations of feature distortions using the AID dataset. Table 5 documents the average classification accuracy for each combination in the five-way few-shot classification. From the table, it is visually evident that the contribution of distortion optimization varies significantly for different types of features. Optimization of edge feature distortion yields the greatest performance gain, followed by texture features, with color features exhibiting the smallest gain. Moreover, as the dimension of feature distortion optimization increases, the model's performance shows varying degrees of gain change depending on the combination of different feature types. To better illustrate the benefits of the dual-path classification strategy, we compared the classification performance between the single path (without branch B and weighting coefficients) and the dual path by recording the classification accuracy for individual categories. Using the AID dataset as an example, we randomly selected five categories related to urban scenes and five unrelated to urban scenes for testing. Each category served as a query for few-shot classification, and after each round of classification for all test objects, a new round began by randomly selecting query images and a support set. This process continued for 200 rounds. Figure 8 presents the confusion matrix of the ODS-DC model for these scene categories under both single-path and dual-path scenarios (five-way, five-shot). As observed from the figure, dual-path classification exhibits a significant improvement over the single path, with substantial variations in the degree of enhancement for each category. The differences in gains are primarily related to the selection of scene types, where specific scenes are more influenced by feature types that exhibit better discriminability. For instance, in the comparison between (A) and (B), the dual-path classification model achieves greater performance improvement in the \"Center\" category, where edge features are more prominent. However, in the comparison between (C) and (D), the \"Forest\" category, where texture features are more pronounced, exhibits the greatest improvement in accuracy. Furthermore, to better capture the variations in the operational performance of the model, we assessed the framework's average prediction time on the AID dataset under 1-shot, 5-shot, and 10-shot (5-way) scenarios. We randomly sampled 20 subsets of classes from the AID dataset for model training. Subsequently, using 100 randomly selected samples from each corresponding subset, we evaluated the model's predictions after each training iteration. Table 6 documents the average inference time for all sampled data under different configurations (95% confidence intervals included). Notably, dual-path classification exhibits a slight decrease in inference speed compared to single-path \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Conv-4 (\%)** & **ResNet-12 (\%)** & **ResNet-50 (\%)** \\ \hline UCM & 61.67 \(\pm\) 0.83 & 77.60 \(\pm\) 0.72 & 79.07 \(\pm\) 0.33 \\ AID & 63.03 \(\pm\) 0.90 & 79.04 \(\pm\) 0.69 & 80.60 \(\pm\) 0.36 \\ NWPU & 65.75 \(\pm\) 0.91 & 84.66 \(\pm\) 0.76 & 85.19 \(\pm\) 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: In the 5-way, 5-shot task, ODS-DC achieves average classification accuracy using different feature extraction networks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Group** & \multicolumn{3}{c}{**Average Accuracy (\%)**} \\ \cline{2-3}  & **1 Shot** & **5 Shot** \\ \hline Edge & 61.02 \(\pm\) 1.51 & 75.58 \(\pm\) 1.32 \\ Texture & 60.85 \(\pm\) 1.83 & 74.10 \(\pm\) 1.50 \\ Color & 57.47 \(\pm\) 1.94 & 73.70 \(\pm\) 1.51 \\ Edge and Color & 62.91 \(\pm\) 1.19 & 77.19 \(\pm\) 1.04 \\ Edge and Texture & 65.11 \(\pm\) 0.97 & 78.67 \(\pm\) 0.80 \\ Texture and Color & 62.74 \(\pm\) 1.72 & 76.15 \(\pm\) 1.29 \\ Edge, Texture, and Color & 66.28 \(\pm\) 0.89 & 79.04 \(\pm\) 0.69 \\ \hline \hline \end{tabular}
\end{table}