# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 1 Introduction  
Some learning paradigms aim to avoid relying on a significant amount of labelled samples. Among them, weakly supervised learning [1, 2], semi-supervised learning [3, 4], and self-supervised learning [5, 6, 7] have more recently attracted the attention of the artificial intelligence (AI) community in the context of deep learning (DL). However, in practice, the traditional supervised learning [8, 9] can still be considered the most popular paradigm of all as it has existed since the early days of machine learning (ML). However, the issue of supervised learning is precisely the usual need for a huge amount of labelled samples so that models can produce a satisfactory performance. Another attempt to address this necessity of a huge amount of labelled data is few-shot learning (FSL) [10, 11]. FSL leverages prior knowledge to quickly generalise to new tasks that have only a few samples with supervised information. There is a huge interest in FSL since few-shot tasks can be defined with a few labelled examples per class (typically one or five samples), the _support set_\(S\), and a _query set_\(Q\) with unlabelled examples. Thus, sampled few-shot tasks are derived based on the test dataset (depending on the situation, it can alsobe from the validation and training sets) where each \(K\)-way \(N_{s}\)-shot task involves sampling \(N_{S}\) labelled samples from each of the \(K\) different classes, also chosen at random. Regarding FSL, two points should be stressed. Firstly, there are basically two types of training: episodic and classical. In the episodic case, training is structured in a series of learning problems (or episodes), and hence the model is trained on such a series of episodes where each episode simulates the FSL scenario with corresponding support \(S\) and query \(Q\) sets [12]. This closely mimics the inference phase within the FSL context, where the model will encounter few-shot tasks with limited supervised samples. Classical training is the standard supervised learning approach where models are trained on labelled datasets to learn fixed parameters for classifying data. There are no episodes or few-shot tasks during the training phase in this type of training. Secondly, the type of inference can be inductive or transductive. In inductive inference within FSL, models are trained on the base set with a large number of examples, and then adaptation happens to new tasks with only a few labelled samples. Meta-learning and metric-based learning are generally considered types of inductive inference approaches within FSL. In the transductive case, at the inference time, models predict the class label jointly for all the unlabelled query samples, rather than for one sample/episode at a time [13]. In other words, in this situation, all unlabelled query examples of a few-shot task are classified simultaneously, unlike inductive methods that process samples individually. The remote sensing (RS) community has also paid attention to FSL [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. Handling synthetic aperture radar (SAR) [16, 17, 25] and hyperspectral [21, 22] images as well as addressing the computer vision tasks of scene classification [15, 19, 20], object detection [23, 24], and semantic segmentation [14] are just a few directions where FSL has been used by such a community. A brief search conducted in August 2024 on the LENS.ORG portal [27] using the search string _'few-shot learning' AND'remote sensing'_, considering the period of August 2015 to August 2024, yielded approximately 450 publications. Venues more related to the RS field with a few of them being part of the general field of AI were taken into account. This shows the growth of FSL within the RS community. Despite these numerous studies, there is still a lack of evidence supporting the suitability of FSL techniques within DL applied for RS.