High-resolution imaging is essential in remote sensing and geoscience for precise environmental and geological analysis. DR-SCAN (Dual-Branch Residual Spatial and Channel Attention Networks), a neural network architecture for image super-resolution across these domains, is introduced. Evaluated on the UCMerced Land Use and DeepRock-SR datasets, DR-SCAN demonstrates adaptability to diverse remote sensing landscapes and effectiveness in resolving pore-scale geological features. Feature map visualizations highlight the model’s ability to prioritize critical spatial features, enhancing interpretability for domain-specific applications.