# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 2 Background
### Related Work  
This section presents some recent studies related to this research. In [15], the authors proposed a ranking-preserving Knowledge Distillation (KD) approach that trains a student network to replicate the ranking of support images produced by teacher networks. By combining multi-teacher KD with learning-to-rank, they developed a new distillation loss based on Plackett-Luce distributions, resulting in a novel few-shot classification model termed the ranking network. As for the experimental part, the datasets used were RESISC-45 [52] and AID [51]. They relied on episodic training while our study chose classical training. Moreover, tasks are of the traditional types: 5-way 1-shot or 5-way 5-shot. To answer the proposed questions, our approach considered a wide variation of unseen classes during the inference phase: from smaller values (2-way) to larger ones (9-way). In [19], a Multi-Grained Global-Local Semantic Feature Fusion (MGGL-SFF) method for FSL remote sensing scene classification, addressing the challenge of complex scenes with hierarchical and coupled spatial relations (e.g., internal and external spatial contexts) that impede feature extraction, was proposed. The MGGL-SFF method effectively combines global discriminative spatial semantic features with local transferable fragment features, establishing a robust prototype representation for FSL. The RESISC-45 was the only dataset used. It is important to note that while the authors selected 15 novel classes for the test set, the experimental setup was limited to evaluating performance on only five of these classes. Thus, tasks are again 5-way 1-shot or 5-way 5-shot. The authors did not apply statistical tests to have conclusions based on more solid foundations. To create the Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification (SGMNet) [20], the authors took into account FSL natural image classification methods. However, they argued that such methods overlook two distinctive features of RS images: (1) object co-occurrence, where multiple objects frequently appear together within a scene, and (2) object spatial correlation, where these co-occurring objects follow specific spatial patterns. To leverage these features, the SGMNet approach was then proposed. This framework includes a scene graph construction module to represent each  
Figure 1: An arrangement for a 2-way 1-shot few-shot task. test image or scene class as a scene graph, with nodes capturing object co-occurrences and edges representing spatial correlations. A scene graph matching module then evaluates the similarity between each test image and each scene class. Regarding the experiments, RESISC-45, AID, UC Merced [49], and WHU-RS19 [50] were the selected datasets. In our study, in addition to these four sets, EuroSAT [47] and XAI4 SAR [48] were also used to have a more significant number of sets of RS images. In the meta-training phase, they considered episodic training rather than the classical one. Few-shot tasks are once more of the types 5-way 1-shot or 5-way 5-shot, and no statistical tests were conducted. Another FSL approach for RS image scene classification was proposed in [32]. It is a class distribution learning method using support samples via an intra-class feature aggregation (IFA) module, intra-class feature homogenisation (IFH) module, and inter-class cross-calibration (ICC) module. The IFA and IFH modules promote the aggregation and even distribution of intra-class features around model-generated prototypes, reducing intra-class distances and preventing extreme samples near decision boundaries. The selected datasets were RESISC-45, UC Merced, and WHU-RS19. The same previous remarks as before regarding the tasks (5-way 1-shot or 5-way 5-shot) and no statistical tests were conducted. High interclass similarity in RS images can lead to classification confusion. To address this issue, the authors of [33] created a double discriminative constraint-based affine non-negative representation for FSL RS scene classification.