Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images - Mohammad Reza Zargar; Mahdi Hasanlou (Environmental Sciences Proceedings, 2024)
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 m, but using them for large areas or multi-temporal analysis of an area brings huge costs. Thus, to tackle these limitations and access free satellite images with a 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 to be 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, and 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 RGB bands to 2.5 m. In the proposed model, several spatial features were extracted to prevent pixelation in the super-resolved image and were utilized in the model computations. Also, since there is no way to obtain higher-resolution (HR) images in the conditions of the Sentinel-2 acquisition image, we preferred to simulate data instead, using 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 37.23 dB, 0.92, and 0.10 radians, respectively.