# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 3 Materials and Methods
### Experiment Design Options and Metric  
This section describes all the design options defined to answer the proposed RQs, as well as the metric chosen to measure the performance of the FSL techniques. In fact, a series of experiments/evaluations were carried out, but it was decided to call them an \"experiment\" for the sake of simplicity. Firstly, classical training was preferred over episodic training. Although there are some arguments against classical training saying it is less effective for FSL because the model is optimised to perform well on tasks with a lot of training data, and may struggle when faced with new tasks with only a few samples, some authors also state some issues of episodic training. For instance, in [12], the researchers investigated the usefulness of episodic training/learning in methods which use non-parametric approaches, such as nearest neighbours, and concluded that the constraints imposed by episodic learning are not necessary but that they in fact lead to a data-inefficient way of exploiting training batches. Moreover, using classical training is more attractive to practitioners from the RS community since this is based on standard supervised learning which, as mentioned before, can still be considered the most popular paradigm in practice. To make the tasks more challenging for the FSL approaches, we dynamically cropped the images of all datasets to a resolution of 128 \(\times\) 128 pixels. The only exception refers to EuroSAT since the resolution of the images/patches is 64 \(\times\) 64 pixels, and thus we took this into account. Furthermore, as for the low (2-way; EuroSAT and XAI4 SAR) and medium (4-way; UC Merced and WHU-RS19) number of unseen classes, 100 images per class were selected. But WHU-RS19 has less than 100 images per class. Thus, horizontal flipping was used and the set was then augmented so that it reached such a quantity of images per class. As for the high number of unseen classes (6-way for AID and 9-way for RESISC-45), 200 images per class were selected. Again, the idea is to make the models' task more difficult, for example, by providing few total samples per class and thus making learning difficult in the training stage. Each dataset was split into the three traditional sets: training, validation, and test. We used approximately 60% of the classes to compose the training set, 20% of the classes for the validation set, and 20% to form the test set. Since it is classical training, the training set was not sampled in the shape of few-shot tasks, but the validation and test datasets were. The number of few-shot tasks was 50 considering the validation set and 1000 for the test dataset. In terms of the number of samples in the support set, the options were \(N_{S}=1\) (1-shot) and \(N_{S}=5\) (5-shot), and recall that the cardinality of such a set is \(|S|=N_{S}\times K\). There were 10 query images for each few-shot task. Table 1 summarises the information about datasets and few-shot tasks. To answer RQ_2, the number of epochs during training was varied as follows: 10, 50, 100, and 200 epochs. Thus, we can grasp an idea about the relationship between the number of epochs used during training and the number of unseen classes during inference. As for RQ_3, we obtained the values of the metric for all 1-shot tasks, considering all datasets and the number of epochs, and compared them to the respective values obtained for the 5-shot tasks. Then, we applied the Shapiro-Wilk test with a significance level \(\alpha=0.05\) to check data normality. A quantile-quantile (Q-Q) plot was used to confirm the results due to the Shapiro-Wilk test. Since in all cases the data happen to be not normal (see Section 4), the non-parametric Wilcoxon test, with the alternative _greater_ and significance level \(\alpha=0.05\), was applied. To answer RQ_4, we saw in which dataset the approaches presented the worst performances. RESISC-45 was the most challenging, and this is somehow expected since here there is the highest number of unseen classes (nine).