# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### Rq_2  
To reason about the relationship between the number of epochs used during training and the quantity of unseen classes during inference, we calculated the improvement in the average accuracies when training with a higher number of epochs compared to the base case with 10 epochs. Tables 11 and 12 present the average of the results with all techniques per dataset and few-shot tasks. In the column Comparison, the meaning is this: a higher number of epochs compared to (\(\longrightarrow\)) the lower (base) number of epochs.  
Tables 11 and 12 show that the improvement when increasing the number of epochs is not very high for the settings with low-number (2-way) unseen classes during the inference phase, i.e., the datasets EuroSAT and XAI4 SAR. The maximum improvement is 18.512% in 5-shot tasks when the number of epochs goes from 10 to 200, considering the EuroSAT set. The decrease in the performance in the XAI4 SAR case (the red values in Table 11) will be discussed later.  
As for the medium number of unseen classes (4-way), i.e., the UC Merced and WHU-RS19 datasets, the increase is better than in the previous comparison but not as much better. The highest improvement is 29.820% in 1-shot tasks with the UC Merced set going from 10 to 200 epochs. The most impressive gains happened with the highest number of unseen classes, i.e., AID (6-way) and RESISC-45 (9-way). Particularly, improvements related to the AID dataset are remarkable reaching 58.412% in 1-shot tasks (from 10 to 200 epochs). The results considering RESISC-45 are comparable to UC Merced but better  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & \multicolumn{3}{c}{**Per Task**} & \multicolumn{3}{c}{**Total**} \\ \hline  & **1 st** & **2 nd** & **1 st** & **2 nd** & **1 st** & **2 nd** \\  & **(1-Shot)** & **(1-Shot)** & **(5-Shot)** & **(5-Shot)** & **1 st** & **2 nd** \\ \hline PNs & 0 & 1 & 1 & 2 & 1 & 3 \\ SS & 1 & 4 & 1 & 6 & 2 & 10 \\ FT & 0 & 2 & 3 & **10** & 3 & **12** \\ BD-CSPN & **9** & 5 & 4 & 3 & 13 & 8 \\ LS & 4 & 4 & 2 & 4 & 6 & 8 \\ PT-MAP & 6 & 1 & 5 & 2 & 11 & 3 \\ TIM & 5 & **10** & **15** & 2 & **20** & **12** \\ TFT & 0 & 1 & 1 & 2 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Number of best and second-best rankings. The values in **bold** emphasise the FSL approach that achieved the highest number of first places (columns 1 st) and second places (columns 2 nd) for each task type (Per Task) and overall (Total).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Comparison** & \multicolumn{2}{c}{**WHU-RS19**} & \multicolumn{2}{c}{**AID**} & \multicolumn{2}{c}{**RESISC-45**} \\ \hline  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline
50 \(\longrightarrow\) 10 & 16.749\% & 11.242\% & 45.791\% & 41.197\% & 18.496\% & 26.429\% \\
100 \(\longrightarrow\) 10 & 17.814\% & 12.530\% & 40.692\% & 43.873\% & 17.648\% & 25.600\% \\
200 \(\longrightarrow\) 10 & 22.140\% & 15.087\% & 58.412\% & 51.887\% & 21.717\% & 32.704\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Average improvement in the average accuracies: WHU-RS19, AID, and RESISC-45.  
where a maximum gain of 32.704% was perceived in the 5-shot tasks (from 10 to 200 epochs). Thus, we conclude that, within classical training, a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase.