# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
## 1 Introduction  
Satellite remote sensing is used in various practical domains, such as mapping, agriculture, environmental protection, land use, urban planning, geology, natural disasters, hydrology, oceanography, meteorology, and many more. In this field, the spatial resolution is a fundamental parameter that is of particular importance for two reasons. The higher the resolution of an image, the more details and information are available for human interpretation and automated machine understanding [1]. However, the goal of providing high spatial resolution devices at the market price is not always the optimal solution [2]. In the past two decades, super-resolution (SR) has been defined as the process of reconstructing and generating a high-resolution (HR) image from one or more low-resolution (LR)images. The challenge with the SR process is fundamentally that it is an ill-posed problem. This means that a single LR image can be derived from several HR images with minor variations, such as changes in camera angle, color, brightness, and so on [3]. Therefore, various approaches have been developed with different constraints and multiple equations to map an LR image to an HR image.  
Today, the Sentinel-2 constellation freely produces multispectral optical data, providing five-day temporal and global spatial coverage. These achievements have led to the creation of a new flow in the field of space businesses [4].  
The SR methods are categorized into multi-image or single-image approaches. In the Multi-Image Super-Resolution (MISR) approach, lost information is extracted from multiple LR images captured from a scene to recover the HR image [5]. However, due to the different angles and passes from which satellite images are acquired for a scene and the sub-pixel misalignments present in these images, the correspondence between a pixel in one image and the same pixel in the next image will not be exact. Therefore, in practical applications, obtaining a sufficient number of images for a scene is challenging [1]. In contrast, in the Single-Image Super-Resolution (SISR) approach, the output HR image is obtained using only one LR image. Therefore, SR can be applied without the need for satellite constellation, which eventually results in substantial cost savings and provides a good opportunity for small platforms, with low resolution and cheap instruments [6].  
With the advent of efficient Deep Learning (DL) methods, the SISR approach gradually became dominant among the approaches [7]. Today, one common technique in SR algorithms is to utilize convolutional neural networks (CNNs). The pioneer in CNN-based algorithms was the super-resolution convolutional neural network (SRCNN) [8], and its enhancement has persisted to this day through various methodologies. On the other hand, Generative Adversarial Networks (GANs) [9] have garnered growing interest within the research community. A network based on GANs has been able to generate outputs that are more photo-realistic, albeit with lower quantitative metrics [2].  
Satellite imagery, unlike standard imagery, suffers from significant landscape at the pixel level due to its lower resolution. Consequently, satellite imagery, such as Sentinel-2 images, is more prone to pixelation artifacts during the SR process. In this context, we have proposed a model that has been able to mitigate the aforementioned problems using spatial features. In fact, in the proposed network, an attempt has been made to obtain better results in satellite images by modifying the input features of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) [10].  
The rest of the paper is organized as follows. In Section 2, we describe datasets and the proposed method. Evaluation metrics, comparative solutions, and experimental results are then gathered in Section 3. Finally, Section 4 provides concluding remarks.