# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 6 Conclusions  
The RS community has been exploiting FSL, and there are several studies in this path. Is spite of that, independent evaluations aimed at providing pieces of evidence that certain solutions are more valuable to the community than others are fundamentally important. This is because the RS community, in general, is more of a consumer of AI to solve its problems, which is perfectly appropriate, and such hints are relevant for the development of research. This is precisely the motivation for this research. We selected several FSL approaches, some inductive and others transductive, which had been evaluated with image datasets that did not contain the characteristics of RS images. A comprehensive evaluation with six datasets created for scene classification was conducted: EuroSAT, XAI4 SAR, UC Merced, WHU-RS19, AID, and RESISC-45. TIM, a transductive approach, proved to be the best solution, while PT-MAP, also transductive, exhibited unstable behaviour. The responses of the four research questions suggested in this study are as follows: (1) transductive approaches are better than inductive ones; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. This research then advocates for further development of transductive approaches in the RS community given their superior performance. Additionally, to address the ongoing challenge of limited labelled data, our findings suggest a need for novel FSL techniques capable of handling a higher number of new classes at inference time. In practical terms, the results suggest for practitioners not to stipulate a large number of epochs in the context of FSL classical training. Additionally, no type of preprocessing activity is necessary regarding the similarity between images of the test set and images of the training set in order to obtain a better performance. These findings can guide researchers and professionals in selecting optimal solutions/strategies for developing their applications demanding few labelled samples. Threats to external validity weaken confidence in generalising experiment results across individuals, settings, and time. Population threats are examples of threats to external validity, and they are related to the representativeness of the experiment samples. In this study, six RS datasets were considered. While six databases have a significant number of samples with characteristics that can represent the scene classification context well, the quantity of selected FSL techniques (eight) cannot be considered totally representative. To combat this limitation, it is necessary to choose more techniques, both inductive and transductive, and perform a new evaluation to see whether the results obtained by this research are still valid. Therefore, future directions include precisely increasing the number of FSL approaches, as just pointed out. It is also possible to augment the number of RS image scene classification datasets, although the current selected six sets represent this task well. Thus, a new robust evaluation should be carried out, and hence it is possible to check whether the results and conclusions of this study still hold. Additionally, other computer vision tasks, such as object detection and semantic segmentation, will be addressed. This research was supported by the _Agencia Espacial Brasileira_ (AEB--Brazilian Space Agency). Not applicable. The datasets used in this research are already publicly available. The reference list of this manuscript includes all articles that provide descriptions of the datasets used. This research was developed within the project _Classificação de imagens e dados via redes neurais profundas para multipli s dominos_ (Image and data classification via Deep neural networks for multiple domainS--**IDeepS**).