Machine Learning Models for EOS SAT-1 Satellite Image Enhancing
Viacheslav Popika, Lidia Lelechenko · 2024
This study delves into the realm of satellite image super-resolution, not only focusing on increasing resolution but also overcoming unique distortions. It addresses the challenges posed by the absence of training data, a common issue encountered in real-world scenarios. Additionally, our methodology places a unique emphasis on maintaining the stability of agro-indices within the super-resolved imagery, recognizing the critical role these indices play in agricultural monitoring. The effectiveness of the proposed approach was validated through testing on real data from the EOS SAT-1 optical satellite.