부산대학교 도서관

In recent years, the application of neural networks in the field of satellite image super-resolution has become increasingly widespread. This paper aims to explore how to utilize images from two completely different data sources (Sentinel-2 and Google Earth Pro) as training data to surpass the spatial resolution limitation of 10 meters provided by the Sentinel-2 satellite and obtain higher resolution image. The Sentinel-2 satellite offers open data with four bands with spatial resolution of 10 meters and a resampling cycle every 5 days. This provides abundant data that can be used as Low-Resolution (LR) images for research. Adopting a multi-image super-resolution processing approach allows for the full utilization of geographical information contained within different images of the same location captured at shorter time intervals, while also eliminating concerns regarding information loss due to weather-affected low-quality images. Google Earth Pro provides images with a resolution of up to 0.15 meters, allowing the High-Resolution (HR) image resolution to be adjusted according to the needs. In our experiments, we proposed Sentinel-2 Google Earth Pro Network (SGNET). We used HR images with a resolution of 2 meters, and through SGNET implementation, a five-fold increase in spatial resolution for Sentinel-2 images was achieved, yielding very satisfactory results.