# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 1 Introduction
Given the increasing significance of AI/DL across various fields such as medicine [28], agriculture [29], financial applications [30], autonomous driving [31], and naturally RS, several important questions remain unanswered, particularly for scene classification in RS images. Some of these research questions (RQs) are as follows:  
1. RQ.1--Which of the inference approaches is more suitable: inductive or transductive? 2. RQ.2--Considering classical training, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? 3. RQ.3--Is relying on 5-shot tasks statistically significantly better than 1-shot ones? 4. RQ.4--Would a higher similarity between unseen classes (during inference) and some of the existing classes in the base training dataset improve technique performance? To support the arguments above, some recent publications proposing FSL approaches for scene classification in RS images [15, 19, 20, 32, 33, 34, 35, 36, 37, 38] are presented in Section 2.1. As detailed in this section of related work, these approaches focus on proposing new FSL techniques, and the articles present comparisons to various other studies. However, there is no emphasis on addressing whether, in general, inductive or transductive techniques are better. The main point is, of course, to demonstrate that the proposed technique performs better compared to several others. To this end, RQ.1 was proposed. Furthermore, the number of interactions (epochs/episodes) is fixed during the training phase. Unlike what is proposed in RQ.2, there are no variations in the number of epochs during training to observe how this relates to the number of unseen classes during the inference phase. The number of unseen classes during inference is fixed at five (\(K=5\)). That is, in all the articles, the tasks are one of these two types: 5-way 1-shot or 5-way 5-shot. This differs from what is proposed in this research, where \(K\) varies from smaller values (2-way) to larger ones (9-way). Although comparisons are made to various other FSL approaches, which is quite interesting, the values presented are usually accuracies without the application of statistical tests to provide a more solid foundation for the superiority of the proposed techniques. This explains the motivation for RQ_3. Finally, none of these recent studies investigated whether a greater similarity between unseen classes (during inference) and some of the existing classes in the training base set actually improves the performance of the approaches, as asked in question RQ_4. To the best of our knowledge, even older FSL approaches for RS image scene classification did not investigate this aspect either. By properly answering these questions, a valuable contribution can be given to the RS community where some pieces of evidence point to the most suitable solutions to be used in practice. This is precisely the objective of this study, where we aim to address some relevant questions in the context of FSL for RS image scene classification. A comprehensive evaluation was conducted considering eight FSL techniques where three are inductive, Prototypical Networks (PNs) [39], SimpleShot (SS) [40], and Fine-Tuning (FT) [41], and five are transductive, Bias Diminishing Cosine Similarity-based Prototypical Network (BD-CSPN) [42], LaplacianShot (LS) [43], Power Transform Maximum A Posteriori (PT-MAP) [44], Transductive Information Maximisation (TIM) [45], and Transductive Fine-Tuning (TFT) [46]. Moreover, six RS image scene classification datasets were considered in different unseen class configurations (\(K\)-way). EuroSAT [47] and XAI4 SAR [48] were used with a lower number of unseen classes (2-way), UC Merced [49] and WHU-RS19 [50] were used with a medium number of unseen classes (4-way), and a higher number of unseen classes were taken into account for AID (6-way) [51] and RESISC-45 (9-way) [52].