Combined person classification with airborne optical sectioning

Indrajit Kurmi, David C. Schedl, Oliver Bimber

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Fully autonomous drones have been demonstrated to find lost or injured persons under strongly occluding forest canopy. Airborne optical sectioning (AOS), a novel synthetic aperture imaging technique, together with deep-learning-based classification enables high detection rates under realistic search-and-rescue conditions. We demonstrate that false detections can be significantly suppressed and true detections boosted by combining classifications from multiple AOS—rather than single—integral images. This improves classification rates especially in the presence of occlusion. To make this possible, we modified the AOS imaging process to support large overlaps between subsequent integrals, enabling real-time and on-board scanning and processing of groundspeeds up to 10 m/s.

Original languageEnglish
Article number3804
Pages (from-to)3804
JournalScientific Reports
Issue number1
Publication statusPublished - 9 Mar 2022
Externally publishedYes


  • Forests
  • Histological Techniques
  • Humans
  • Optical Imaging
  • Optical Phenomena


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