# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 3 Materials and Methods
### Datasets  
This section briefly describes the six RS scene classification datasets selected for this research. The number of unseen classes (\(K\)-way) during the inference phase was varied.  
Figure 2: The workflow of the method related to this study.  
EuroSAT [47] and XAI4 SAR [48] were used with a 2-way setting, which is a low number of unseen classes during the inference phase. Sentinel-2 images were used to create EuroSAT, a dataset encompassing 13 spectral bands and featuring 10 classes, with a total of 27,000 labelled and geo-referenced images. The resolution of the images (patches) is 64 \(\times\) 64 pixels. XAI4 SAR consists of Sentinel-1 SAR image patches, including both single look complex (SLC) and ground range detected (GRD) products. Seven sea-ice types are annotated, with GRD image patches having a resolution of 256 \(\times\) 256 pixels. Additionally, a Gaofen-3 SAR scene covering a large urban area was used, with seven land use and land cover (LULC) classes annotated. Figure 3 presents samples of some classes from the EuroSAT and XAI4 SAR datasets.  
In terms of a medium number of unseen classes, the UC Merced [49] and WHU-RS19 [50] datasets were considered with a 4-way configuration. UC Merced is a set with 21 classes where there are 100 images for each of the 21 classes. Each image (patch) has a resolution of 256 \(\times\) 256 pixels. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the USA. WHU-RS19 is a collection of satellite images sourced from Google Earth, providing high-resolution imagery with a spatial resolution of up to 0.5 m. The dataset encompasses 19 distinct scene classes including _airport_, _beach_, _bridge_, _commercial_, _desert_, _farmland_, _football field_, _forest_, and others. For each class, there are about 50 samples. The dataset includes images from various regions and resolutions, leading to potential differences in scale, orientation, and illumination within each class. Figure 4 presents samples of some classes from the UC Merced and WHU-RS19 datasets.  
With AID [51] and RESISC-45 [52], the option was to have a higher number of unseen classes: 6-way and 9-way, respectively. AID is a large-scale dataset designed to push forward image scene classification within RS. AID consists of over 10,000 aerial scene images that were carefully collected and annotated. Sample images were collected from Google Earth imagery, and there are 30 aerial scene types: _airport_, _bare land_, _baseball field_, _beach_, _bridge_, _centre_, _church_, _commercial_, _dense residential_, _desert_, _farmland_, _forest_, _industrial_, _meadow_, _medium residential_, _mountain_, _park_, _parking_, _playground_, _pond_, _port_, _railway station_, _resort_, _river_, _school_, _sparse residential_, _square_, _stadium_, _storage tanks_, and _viaduct_. Image resolution is 600 \(\times\) 600 pixels.  
Figure 3: Samples from EuroSAT (top row: (**a–e**)) and XAI4 SAR (bottom row: (**f–j**)) datasets.  
RESISC-45 is another large-scale dataset for RS image scene classification. Such a dataset includes 31,500 images spanning 45 scene classes, with 700 images per class. RESISC-45 is characterised by its large scale, both in terms of scene classes and total image count, and significant variations in factors such as translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion. Image resolution (dimension) is 256 \(\times\) 256 pixels. Figure 5 presents samples of some classes from the AID and RESISC-45 datasets.  
As remarked in Section 2.1, the RESISC-45, AID, WHU-RS19, and UC Merced datasets are very traditional and they have been used in many studies. This explains the reason behind their selection. EuroSAT was chosen due to the lower resolution (dimension) of its images, and XAI4 SAR was aimed at considering a different type of image, i.e., SAR images.