# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 1 Introduction  
Detecting ships for civil and military activities in coastal areas via computer vision techniques has gained tremendous attention, followed by technological advancements in remote sensing. The abundance of Earth observation satellites and sensors with various spatial and temporal resolutions promoted studies regarding ship detection from remotely sensed imageries. In normal circumstances, ships weighing more than 300 tonnages can be monitored by transponder widgets such as an Automatic Identification System (AIS) [1]. However, AIS is only effective as long as its active state is preserved. Therefore, alternative options were taken into consideration for surveillance [2]. Radar and optical satellite images as a substitution for AIS became popular since sea objects in coastal areas can be monitored in defined intervals without human intervention and regardless of tonnage limit. The combination of deep learning with computer vision techniques has recently brought practicality to detecting ships. Usage of remotely sensed imageries, for that matter, necessitated the extraction of ship instances with state-of-the-art deep learning methods due to practical components such as faster convergence, a smaller amount of time spent on detection, and less computational capacity. These methods for the rapid detection of ships in the remote sensing field were benefited in some studies [2, 3, 4, 5, 6, 7]. Optic and radar satelliteimages have been at the forefront of these detection studies [8; 9; 10; 11; 12]. Optical satellite images have an important characteristic that differentiates them from radar images. Optical remote sensing images are conducive to the human eye, meaning the interpretation of features is easier than that of radar images [13; 14]. When radar images are collected in hazy weather conditions, the spectral signatures of ships are negatively affected, leading to poorer ship detection performance. In addition, inshore ship detection cannot be performed from radar images [15]. Therefore, optical remote sensing images are valuable Earth data sources in ship detection studies. However, most optical remote sensing images offered free of charge are at medium spatial resolution, referring to fewer features in each pixel. As one of the solutions, super-resolution enables feature recovery and is yet to be explored concerning the degree of recovery in medium-low spatial resolution satellite images. Many studies experimenting with super-resolution algorithms to enhance the visual quality of satellite images before implementing a computer-vision task exist in the literature [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. In these studies utilizing super-resolution as a pre-processing step before the implementation of computer-vision tasks, satellite images at multiple spatial resolutions ranging from 0.2 m to 30 m were visually enhanced [30]. [PERSON] and others enhanced satellite images ranging from 30 cm to 4.8 m spatial resolution with a Very Deep Super-Resolution (VDSR). They experimented with the detectors of You Only Look Once (YOLO) and Single Shot Detector (SSD) to observe the impact of super-resolution on the detectability of various objects such as boats, airplanes, trucks, and other small vehicles [31]. This was a pioneering study since it assessed the impact of super-resolution techniques on detecting objects belonging to different classes on satellite images across multiple resolutions. Moreover, it was an important example of effectively detecting smaller objects, such as boats and small vehicles, with the VDSR framework. [PERSON] and the others conducted an object detection study in which a Multi-Class Cyclic Super-Resolution Generative Adversarial Network (MCGR) was developed and utilized by super-resolving satellite images two times and four times in order to achieve better detection metric results [32].