Aircraft Detection and State Estimation

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Publication date2024
Pages7693-7676
ISBN (Electronic)979-8-3503-6032-5
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium
Country/TerritoryGreece
CityAthens
Period07/07/202412/07/2024
SeriesIEEE International Geoscience and Remote Sensing Symposium Proceedings
ISSN2153-7003

Keywords

  • Aircraft
  • ADS-B
  • Deep learning
  • Object detection
  • State estimation
  • Multispectral image
  • Satellite image

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