Titolo della tesi: Super-Resolution of Sentinel-2 Imagery for Remote Sensing Applications
The increasing demand for high-resolution satellite imagery in various applications such as environmental monitoring, urban planning, precision agriculture, and disaster response has led to significant advancements in super-resolution (SR) techniques. This research focuses on enhancing the spatial resolution of Sentinel-2 imagery using deep learning-based SR methodologies, leveraging convoluneural networks (CNNs) and generative adversarial networks (GANs). The study evaluates the effectiveness of these techniques in improving image clarity and usability for remote sensing applications.