Enhancing the Spatial Resolution of Sentinel-2 Images Through Super-Resolution Using Transformer-Based Deep-Learning Models

Satellite imagery plays a pivotal role in environmental monitoring, urban planning, and national security. However, spatial resolution limitations of current satellite sensors restrict the clarity and usability of captured images. This study introduces a novel transformer-based deep learning model to enhance the spatial resolution of Sentinel-2 images. The proposed architecture leverages Multi-Head Attention and integrated Spatial and Channel Attention mechanisms to effectively extract and reconstruct fine details from low-resolution inputs. The model's performance was evaluated on the Sentinel-2 dataset, along with benchmark datasets (AID and UC-Merced), and compared against state-of-the-art methods,…