@inproceedings{8c1a25a2d8d24127ad2a2776cccaf6c6,
title = "Aircraft Detection and State Estimation",
abstract = "Unidentified flying objects can be aircraft that do not continuously broadcast ADS-B. They pose a risk for air traffic safety, territorial violation, espionage, etc. 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 estimate the aircraft state vector i.e. position, velocity, heading, altitude. This work allows real-time monitoring of flying objects in satellite images.",
keywords = "Aircraft, ADS-B, Deep learning, Object detection, State estimation, Multispectral image, Satellite image",
author = "Peder Heiselberg and S{\o}rensen, {Kristian A.} and Henning Heiselberg",
year = "2024",
doi = "10.1109/IGARSS53475.2024.10642839",
language = "English",
series = "IEEE International Geoscience and Remote Sensing Symposium Proceedings",
pages = "7693--7676",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium",
publisher = "IEEE",
address = "United States",
note = "2024 IEEE International Geoscience and Remote Sensing Symposium , IGARSS 2024 ; Conference date: 07-07-2024 Through 12-07-2024",
}