# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### RQ_1
\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 & 0.509 & 0.526 & 0.618 & 0.674 & 0.571 & 0.687 \\ SS & **0.512** & **0.527** & 0.650 & 0.712 & 0.552 & 0.666 \\ FT & 0.510 & 0.518 & 0.670 & 0.767 & 0.548 & 0.659 \\ BD-CSPN & 0.501 & 0.520 & 0.656 & 0.705 & 0.557 & 0.663 \\ LS & 0.505 & 0.523 & 0.600 & 0.630 & 0.570 & 0.685 \\ PT-MAP & 0.492 & 0.507 & 0.654 & 0.748 & **0.655** & **0.742** \\ TIM & 0.510 & 0.524 & **0.694** & **0.769** & 0.557 & 0.674 \\ TFT & 0.509 & 0.526 & 0.618 & 0.679 & 0.571 & 0.687 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average accuracy, 10 epochs: EuroSAT, XAI4 SAR, and UC Merced. As for 10 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**. In **bold** and blue, the best result for all datasets is presented (1-shot tasks). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & \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 PNs & 0.649 & 0.775 & 0.405 & 0.528 & 0.368 & 0.474 \\ SS & 0.674 & 0.782 & 0.427 & 0.537 & 0.389 & 0.480 \\ FT & 0.668 & 0.785 & 0.424 & 0.538 & 0.387 & 0.487 \\ BD-CSPN & **0.682** & 0.787 & 0.422 & 0.518 & 0.386 & 0.472 \\ LS & 0.670 & 0.785 & 0.415 & 0.506 & 0.381 & 0.465 \\ PT-MAP & 0.679 & 0.785 & 0.416 & 0.505 & **0.399** & **0.502** \\ TIM & 0.679 & **0.787** & **0.428** & **0.543** & 0.393 & 0.485 \\ TFT & 0.649 & 0.776 & 0.405 & 0.528 & 0.367 & 0.475 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average accuracy, 10 epochs: WHU-RS19, AID, and RESISC-45. As for 10 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**. In **bold** and blue, the best result for all datasets is presented (5-shot tasks). was FT (inductive), tied with TIM, in the 5-shot tasks and the RESISC-45 dataset. LS and, once again, TIM were the highlights here. Furthermore, a pattern begins to emerge from this point. The WHU-RS19 dataset appears to be the \"easiest\" of all, where, proportionally and when analysing the shot task type separately, the best results are obtained. This can be explained by the low variability of images within the classes, as data augmentation was necessary in this case. On the other hand, the RESISC-45 dataset stands out as the \"most difficult\", likely due to the larger number of unknown classes during the inference phase. As for 200 epochs, see Tables 8 and 9, the same trend of performances is shown. The transductive approaches presented better results overall, with only in two cases an inductive technique, FT, being the best: 5-shot tasks with WHU-RS19 tied with BD-CSPN and TIM, \begin{table}
\begin{tabular}{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 & 0.538 & 0.590 & 0.633 & 0.722 & 0.669 & 0.816 \\ SS & 0.541 & 0.589 & 0.699 & 0.806 & 0.721 & 0.849 \\ FT & 0.540 & 0.595 & 0.680 & 0.805 & 0.721 & 0.852 \\ BD-CSPN & 0.536 & 0.581 & 0.688 & 0.803 & 0.734 & 0.847 \\ LS & 0.529 & 0.576 & 0.639 & 0.719 & 0.723 & 0.828 \\ PT-MAP & **0.552** & **0.606** & 0.620 & 0.707 & **0.777** & 0.854 \\ TIM & 0.545 & 0.595 & **0.719** & **0.815** & 0.728 & **0.858** \\ TFT & 0.538 & 0.590 & 0.632 & 0.724 & 0.669 & 0.816 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average accuracy, 100 epochs: EuroSAT, XAI4 SAR, and UC Merced. As for 100 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**. In **bold** and \(\text{blue}\), the best result for all datasets is presented (1-shot and 5-shot tasks).