## 1 Introduction  
Super-resolution (SR) techniques are used to improve low-quality images by turning them into high-resolution (HR) data. Information considered to be in the HR image but lost in the low-resolution (LR) one is restored [1, 2, 3]. The studies carried out for this purpose have moved to another dimension with the use of deep learning (DL) models. DL-based SR models [4, 5] have shown significant development after the Super-Resolution Convolutional Neural Network (SRCNN) was proposed in 2014 [6]. Over the years, linear learning [6, 7, 8], residual learning [9, 10, 11], recursive learning [12, 13, 14], adversarial networks [15, 16], and attention structures [17, 18] have been used for resolution enhancement purposes on various datasets [4]. While many models [19, 20] have aimed to achieve close or better performance values with a lighter structure than their predecessors, others have prioritized the quality of the end product directly, and do not rely on the number of parameters [10, 21]. The majority of SR studies have used the T91 [22] and DIV2K [23] datasets for training, with the Set5 [24] and Set14 [25] datasets being used for testing purposes. In addition, datasets have been diversified with applications conducted by different disciplines. Several datasets have been created to solve real-world problems [26, 27]. As models are built on frequently used common datasets, they do not always produce the expected results in independent applications with different scene characteristics [27, 28]. When real-world problems are focused within the scope of SR, datasets containing remotely sensed images and magnetic resonance images [29] come to the fore. These include both those can be directly used [30; 31; 32] and those that have been created by researchers specifically for relevant studies [26; 33]. While satellite images are most commonly favored as remotely sensed images for resolution improvement, aerial photographs obtained by platforms such as UAVs and aircraft have been used as well [34]. A common element of these datasets that distinguishes them from others is their focus on solving real-world problems. Significant progress has been achieved in applications including both commonly used datasets and those focusing on solving real-world challenges. The images enhanced with SR techniques have been brought very close to their HR equivalents. In particular, the Enhanced Deep Super-Resolution (EDSR) [10] network has been a turning point in the temporal hierarchy of SR approaches. Successful results have been achieved even at high scale factors with the Residual Dense Network (RDN) [35], providing better performance than the EDSR model with fewer parameters, as well as with the attention structures [17; 18] built on these models. Thus, there is not much room left for a purely algorithm-based approach using current datasets to show significant progress compared to previous studies. One of the dilemmas in SR studies in the literature is the distinguishability level between the images used in the training phase and those to be increased in the test phase. In many studies, the LR images presented to be enhanced are to some extent interpretable and analyzable. In this respect, studies using satellite images have made a difference in terms of SR. There are studies that directly use models created on commonly used datasets in optical images [36] as well as SR approaches specifically designed for enhancing satellite images [37; 38]. Although some studies aim to improve the details that can already be distinguished in LR images [33], others that use satellite images such as Landsat and Sentinel may be insufficient for high-level detail extraction and scene analysis [39]. In particular, enhancing Landsat images, which provide older data compared to Sentinel, has allowed the expansion of time intervals for multi-temporal land use/cover (LULC) change detection studies. Previous research reported higher accuracy in classifications conducted with the enhanced images [40].