# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### RQ_1  
The average accuracies for training with 10 epochs are shown in Table 2 (the EuroSAT, XAI4 SAR, and UC Merced datasets) and Table 3 (the WHU-RS19, AID, and RESISC-45 datasets). In **bold**, we see the best performance for each shot configuration of a task (1-shot, 5-shot) considering each dataset. For instance, SS was the best approach not only for 1-shot (0.512) but also for 5-shot (0.527) tasks considering the EuroSAT set. TIM was the best for both configurations (1-shot: 0.428; 5-shot: 0.543) taking into account the AID dataset. Moreover, in **bold** and blue we present the best result for all datasets. Thus, TIM obtained the highest average accuracy for the 1-shot tasks considering all datasets with 10 epochs: 0.694 with the XAI4 SAR dataset. As for the 5-shot case, BS-CSPN and TIM tied with the highest value (0.787) with the WHU-RS19 dataset. It is clear that the transductive approaches presented better performances than the inductive counterparts. Only for the EuroSAT dataset did an inductive technique (SS) exhibit a superior performance. For all other cases, transductive approaches were superior, with TIM and PT-MAP standing out. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Cl Training** & **\#Cl Val** & **\#Cl Test** & **\#Tasks Val** & **\#Tasks Test** \\ \hline EuroSAT & 6 & 2 & 2 & 50 & 1000 \\ XAI4 SAR & 4 & 2 & 2 & 50 & 1000 \\ UC Merced & 13 & 4 & 4 & 50 & 1000 \\ WHU-RS19 & 11 & 4 & 4 & 50 & 1000 \\ AID & 18 & 6 & 6 & 50 & 1000 \\ RESISC-45 & 27 & 9 & 9 & 50 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information about datasets and few-shot tasks. Caption: Cl = classes; Val = validation. Tables 4 and 5 show the results for 50 epochs. PNs, an inductive approach, and TFT, a transductive one, were the best considering 5-shot tasks and the WHU-RS19 dataset. And their result (0.886) was the highest taking into account all datasets and 50 epochs. As for the 1-shot case, the highest of all values was due to LS (0.881) evaluated on the WHU-RS19. Again, transductive solutions were quite superior where, once again, TIM exhibited the best results in several cases, but BD-CSPN also performed significantly well in this scenario. By increasing the number of epochs, the transductive techniques reaffirm their best performances. In the case of 100 epochs, see Tables 6 and 7, most of the best results were obtained by transductive approaches, where the best performance across all datasets was achieved by LS, both for 1-shot tasks (0.834) and for 5-shot tasks (0.896). The exception \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & \multicolumn{2}{c}{**EuroSAT**} & \multicolumn{2}{c}{**XAI4 SAR**} & \multicolumn{2}{c}{**UC Merced**} \\ \hline  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline PNs & 0.525 & 0.558 & 0.535 & 0.573 & 0.623 & 0.744 \\ SS & 0.527 & 0.563 & 0.577 & 0.662 & 0.659 & 0.760 \\ FT & 0.526 & 0.566 & 0.568 & 0.654 & 0.657 & 0.760 \\ BD-CSPN & 0.521 & 0.555 & 0.559 & 0.633 & 0.661 & 0.735 \\ LS & 0.513 & 0.540 & 0.520 & 0.543 & 0.652 & 0.722 \\ PT-MAP & 0.525 & 0.553 & 0.522 & 0.547 & **0.756** & **0.829** \\ TIM & **0.529** & **0.569** & **0.584** & **0.686** & 0.664 & 0.772 \\ TFT & 0.525 & 0.559 & 0.534 & 0.574 & 0.623 & 0.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average accuracy, 50 epochs: EuroSAT, XAI4 SAR, and UC Merced. As for 50 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**.