# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### Rq_4  
RESISC-45 was the dataset where the FSL techniques faced the greatest difficulty. The \(O\)r9 subset was defined with the following classes of the training set: \(O\)r9 = {_airport_, _basketball court_, _bridge_, _church_, _commercial area_, _golf course_, _harbour_, _intersection_, _island_}. By calculating the structural similarity (SSIM) with all other RESISC-45 classes, the scenario (the new test dataset) _More_, containing the most similar classes, resulted in the following: \(More=\{\)_meadow_, _rectangular farmland_, _runway_, _circular farmland_, _baseball diamond_, _lake_, _ship_, _river_, _cloud_}. On the other hand, the scenario (the new test dataset) with the less similar classes is \(Less=\{\)_snowberg_, _dense residential_, _chaparral_, _mobile home park_, _place_, _railway station_, _storage tank_, _industrial area_, _medium residential_}. Considering both tasks (1-shot and 5-shot) and all the number of epochs, the three sets do not follow a normal distribution as before, as shown by the application of the Shapiro-Wilk test and confirmation by Q-Q plots. The results of the non-parametric Wilcoxon test, with the alternative _greater_ and paired samples d = \(O\)r9 \(-\)_More_, were as follows: statistic = 1094.5 and \(p\)-value = 1.018490 \(\times\) 10\({}^{-17}\). The mean value of the \(O\)r9 set is 0.473309 while for the _More_ set is 0.438200. Thus, there is strong statistical evidence that the values in the \(O\)r9 set are consistently and significantly greater than those in the _More_ set. In other words, if the test set has classes that are more similar to some classes in the training set, this not only fails to improve the performance of the techniques but, in fact, there is evidence that randomly selecting the classes in the test set is much better. As for the \(Less\) scenario, the outcomes of the non-parametric Wilcoxon test, with the alternative _greater_ and paired samples d = \(O\)r9 \(-\)_Less_, were as follows: statistic = 1176 and \(p\)-value = 8.115114 \(\times\) 10\({}^{-10}\). The mean values are as follows: \(O\)r9 = 0.473309 and \(Less=0.426466\). Again, there is strong statistical evidence that the values in the \(O\)r9 set are consistently better than the ones in the \(Less\) set. The results per type of shot task were obtained too. Again, not only for the 1-shot but also for the 5-shot tasks, all sets do not follow the normal distribution. As for the 1-shot tasks, these were the outcomes of the Wilcoxon test for the _More_ scenario, with the alternative _greater_ and paired samples d = \(O\)r9 \(-\)_More_: statistic = 300, \(p\)-value = 5.960465 \(\times\) 10\({}^{-8}\), mean \(O\)r9 = 0.408720, and mean \(More=0.364363\). As for the \(Less\) scenario, paired samples d = \(O\)r9 \(-\)_Less_: statistic = 300, \(p\)-value = 5.960465 \(\times\) 10\({}^{-8}\), mean \(Or\)9 = 0.408719, and mean \(Less=0.348023\). Finally, the results of the Wilcoxon test for the 5-shot tasks are presented. As for the _More_ scenario, they were as follows: statistic = 229, \(p\)-value = 1.146602 \(\times\) 10\({}^{-2}\), mean \(Or\)9 = 0.537898, and mean \(More=0.512037\). As for the \(Less\) scenario, they were as follows: statistic = 300, \(p\)-value = 5.960465 \(\times\) 10\({}^{-8}\), mean \(Or\)9 = 0.537898, and mean \(Less=0.504910\). Once again, we observe the same previous conclusions: the \(O\)r9 set presents values that are statistically significantly better than the respective values in the \(More\) and \(Less\) sets, both for the 1-shot and 5-shot tasks. The \(More\) and \(Less\) sets were also compared directly. When considering all tasks (both 1-shot and 5-shot) and 1-shot tasks alone, the \(More\) set performed significantly better than the \(Less\) set from a statistical standpoint. However, for 5-shot tasks alone, there was no statistically significant difference between the two sets. Consequently, it is not definitively possible to conclude that, when comparing the most extreme cases, a scenario with highly similar images consistently outperforms one with less similar images.