# 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}{**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 & 0.763 & 0.892 & 0.578 & 0.771 & 0.418 & 0.594 \\ SS & 0.783 & 0.882 & 0.599 & 0.770 & 0.467 & 0.619 \\ FT & 0.786 & 0.889 & 0.597 & 0.765 & 0.469 & **0.624** \\ BD-CSPN & 0.832 & 0.893 & 0.623 & 0.779 & **0.480** & 0.616 \\ LS & **0.834** & **0.896** & **0.640** & **0.784** & 0.463 & 0.592 \\ PT-MAP & 0.746 & 0.813 & 0.482 & 0.633 & 0.425 & 0.554 \\ TIM & 0.794 & 0.889 & 0.607 & 0.775 & 0.473 & **0.624** \\ TFT & 0.763 & 0.892 & 0.577 & 0.771 & 0.417 & 0.594 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average accuracy, 100 epochs: WHU-RS19, AID, and RESISC-45. 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). \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 & 0.774 & 0.886 & 0.570 & 0.722 & 0.422 & 0.594 \\ SS & 0.773 & 0.866 & 0.634 & 0.714 & 0.482 & 0.635 \\ FT & 0.775 & 0.870 & 0.634 & 0.775 & 0.481 & 0.638 \\ BD-CSPN & 0.807 & 0.871 & **0.658** & **0.779** & **0.490** & 0.635 \\ LS & **0.811** & 0.879 & 0.656 & 0.772 & 0.463 & 0.600 \\ PT-MAP & 0.749 & 0.833 & 0.481 & 0.631 & 0.391 & 0.515 \\ TIM & 0.782 & 0.874 & 0.640 & **0.779** & 0.487 & **0.639** \\ TFT & 0.774 & **0.886** & 0.602 & 0.762 & 0.422 & 0.594 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy, 50 epochs: WHU-RS19, AID, and RESISC-45. As for 50 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). and 5-shot tasks with RESISC-45. TIM once again had some better performances, but the highlight here was BD-CSPN, which achieved the highest number of first-place rankings. Table 9 shows the extreme results of the evaluation. The cells with green background mean that these are the best results considering all the number of epochs. Thus, with 200 epochs, the best values of average accuracies overall for both tasks occurred with the WHU-RS19 dataset. But, the cells with a yellow background and values in red show that PT-MAP with RESISC-45 presented the worst results for both tasks and with 200 epochs. Table 10 summarises the number of times a certain approach achieved the best result, as well as when it was the second best. While BD-CSPN obtained the highest number of first positions for 1-shot tasks, TIM was the most outstanding considering 5-shot tasks and obtained the highest number of second-best places for 1-shot ones. Altogether, we can state that TIM was the best of all approaches, followed by BD-CSPN, PT-MAP (but see the discussion later), and LS. All of these techniques are transductive. The first inductive technique appears in fifth place (FT). This conclusion confirms the results presented for other domains: transductive approaches outperform inductive ones in the context of RS image scene classification.