The detection of tiny objects in satellite imagery is a critical task with wide-ranging applications, including environmental monitoring, urban planning, disaster response, and the surveillance of critical transport infrastructure. Sentinel-2 satellite data, characterized by providing rich spectral information at a moderate spatial resolution (10–60m), poses significant challenges for the identification of small-scale features due to limited spatial detail and the effects of mixed pixels. This study investigates the potential of super-resolution techniques to enhance Sentinel-2 imagery for improved tiny object detection. A dataset was meticulously annotated to identify aircraft across diverse areas of interest, enabling rigorous evaluation using advanced methodologies. Detection was performed using a hybrid approach that combines a YOLOv8-based object detector and a vision-transformer-based object density estimator. The fusion of these complementary methods significantly reduces false positives, resulting in improvements in precision and F1 score. The findings underscore that super-resolved Sentinel-2 imagery offers a viable and cost-effective solution for detecting tiny objects, particularly in scenarios where access to high-resolution imagery is restricted or economically prohibitive.