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The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the inherent complexity of these architectures/data often makes these methods very difficult to train. Despite these recent advances, the huge amount of network parameters that must be fine-tuned and the lack of suitable high-resolution remotely sensed imagery in actual operational scenarios still raise some important challenges that may become relevant limitations in the existent earth observation data production environments. To address these problems, we propose a new remote sensing SR approach that integrates a visual attention mechanism within a residual-based network design in order to allow the SR process to focus on those features extracted from land-cover components that require more …