Abstract
Unidentified flying objects are aircraft that do not continuously broadcast ADS-B. They pose a risk to air traffic safety. In this study, we introduce a method for detecting and estimating the state of aircraft in Sentinel-2 multispectral satellite images. We construct a dataset of 579 ADS-B annotated aircraft from 69 Sentinel-2 images. A CNN is trained on the dataset to aircraft state vector i.e. position, velocity, heading, and altitude. This work allows real-time monitoring of flying objects in satellite images.
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