Generating Super Spatial Resolution Products Form Sentinel-2 Satellite Images

Access to high spatial resolution satellite images enables more accurate and detailed analysis of these images. Furthermore, it facilitates easier decision-making on a wide range of issues. Nevertheless, there are commercial satellites such as Worldview that have provided a spatial resolution of fewer than 2.0 meters, but using them for large areas or multi-temporal analysis of an area brings huge costs. So, to tackle these limitations and access free satellite images with higher spatial resolution, there are challenges that are known as single-image super-resolution (SISR). The Sentinel-2 satellites were launched by the European Space Agency (ESA) to monitor the Earth which has enabled access to free multi-spectral images, five-day time coverage, and global spatial coverage has been among the achievements of this launch. Also, it led to the creation of a new flow in the field of space businesses. These satellites have provided bands with various spatial resolutions which the Red, Green, Blue, and NIR bands have the highest spatial resolution by 10 m. In this study, therefore, to recover high-frequency details, increase the spatial resolution, and cut down costs Sentinel-2 images have been considered. Additionally, a model based on Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) has been introduced to increase the resolution of 10 m bands to 2.5 m. In the proposed model, to preserve the spectral information of the images, changes were made in the loss function. Also, since there is no way to obtain higher-resolution (HR) images in the conditions of the Sentinel-2 acquisition image, we preferred instead to simulate data, to use a sensor with a higher spatial resolution that is similar in spectral bands to Sentinel-2 as a reference and HR image. Hence, Sentinel-Worldview image pairs were prepared and the network was trained. Finally, the evaluation of the results obtained, showed that while maintaining the visual appearance, it was able to maintain some spectral features of the image as well. The average Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spectral Angle Mapper (SAM) metrics of the proposed model from the test dataset were 42.20 dB, 0.91, 0.08 radians, respectively.