Tiny Object Detection in Super-Resolved Sentinel-2 Imagery

The detection of tiny objects in satellite imagery is a critical task with wideranging 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 smallscale features due to limited spatial detail and the effects of mixed pixels. This study investigates the potential of superresolution 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 YOLOv8based object detector and a visiontransformerbased 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 superresolved Sentinel-2 imagery offers a viable and costeffective solution for detecting tiny objects, particularly in scenarios where access to highresolution imagery is restricted or economically prohibitive.