# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 2 Data and Methodology  
### Data  
This study utilized two types of optical satellite data: Sentinel-2A and PlanetScope imagery. Sentinel-2A images have been freely available to researchers through the European Union's Copernicus Program, which is designed to monitor Earth's land and coastal areas. Sentinel-2A images contain 13 spectral bands, with a spatial resolution of 10 m for the red, green, and blue (RGB) bands, providing medium spatial resolution. The Sentinel-2 satellite's temporal resolution is 10 days, and the combined constellation's temporal resolution is 5 days. On the other hand, PlanetScope images are obtained from a constellation of Dove nanosatellites, which comprises over 180 satellites providing data. Access to PlanetScope images incurs a fee, with the pricing policy based on the area covered. These images consist of four spectral bands and have a high spatial resolution of 3 m for the RGB bands, and 1 day temporal resolution. The accurate color composites produced from RGB imagery are advantageous for interpreting results, as the human eye easily understands them and includes the visible spectrum at this higher resolution [33]. For this study, RGB bands were preferred for both satellite images. High spatial resolution PlanetScope images (3 m spatial resolution) were used to enhance the medium spatial resolution Sentinel-2A satellite images (10 m spatial resolution) via a super-resolution model. In this context, two datasets were generated: Dataset-1 and Dataset-2. Dataset-1 comprises high spatial resolution satellite images used to train the super-resolution model and consists exclusively of PlanetScope images. These images were obtained from Planet Explorer, a service provided by Planet Labs. Dataset-2 consists of medium spatial resolution satellite images derived from Sentinel-2A images. Dataset-2, which encompasses the entire year and includes all selected regions, consists of data from 18 ports spanning the years 2017 to 2024. These images were acquired via the geemap Python package to generate Dataset-2 [34]. The satellite images were acquired from various ports located throughout the Northern Hemisphere. Sample images from both Dataset-1 and Dataset-2 are included in Figure 1. Special attention was given to selecting study locations based on atmospheric conditions, as optical remotely sensed images are highly sensitive to atmospheric disturbances. Consequently, locations with less than 10% cloud coverage were prioritized to ensure the reliability of the collected data.  
Figure 1: Sample images from Dataset-1 and Dataset-2.  
Many benchmark datasets can be used to perform ship detection, such as HRSC2016 [35], xView [36], and others [37; 38; 39; 40; 41]. Although they can aid in increasing the sample size of Dataset-2, they were not integrated into our dataset (Dataset-2) because these image datasets are offered in different sensors, resolutions, and file formats. Within the study, Sentinel-2A images were enhanced via the high-resolution PlanetScope imagery, where the scope of the study is the whole globe.  
Special care was taken to prepare Dataset-1 and Dataset-2 by considering this criterion. Many ports in the Far East, such as Hong Kong, Rizhao, and Tianjin, are at the top of the busiest container ports list, with thousands of ship calls [42]. Detailed information about the ports is given in Table 1.