Remote Sensing Image Super-Resolution using Deep Learning

Remote sensing technologies are integral to monitoring and analyzing Earth's surface, yet the effective extraction of information relies heavily on high-resolution imagery. This study proposes an innovative method for enhancing the spatial resolution of remote sensing images using the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) deep learning architecture. Adapted specifically for remote sensing applications, the model is trained on a dataset of low-resolution and high-resolution images, allowing it to learn the intricate relationships between them. The methodology addresses challenges such as limited training data and complex atmospheric conditions, offering promising solutions for enhancing the resolution of remote sensing imagery.