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Airborne Optical Sectioning

Research output: Contribution to journalArticlepeer-review

Abstract

Drones are becoming increasingly popular for remote sensing of landscapes in archeology, cultural heritage, forestry, and other disciplines. They are more efficient than airplanes for capturing small areas, of up to several hundred square meters. LiDAR (light detection and ranging) and photogrammetry have been applied together with drones to achieve 3D reconstruction. With airborne optical sectioning (AOS), we present a radically different approach that is based on an old idea: synthetic aperture imaging. Rather than measuring, computing, and rendering 3D point clouds or triangulated 3D meshes, we apply image-based rendering for 3D visualization. In contrast to photogrammetry, AOS does not suffer from inaccurate correspondence matches and long processing times. It is cheaper than LiDAR, delivers surface color information, and has the potential to achieve high sampling resolutions. AOS samples the optical signal of wide synthetic apertures (30–100 m diameter) with unstructured video images recorded from a low-cost camera drone to support optical sectioning by image integration. The wide aperture signal results in a shallow depth of field and consequently in a strong blur of out-of-focus occluders, while images of points in focus remain clearly visible. Shifting focus computationally towards the ground allows optical slicing through dense occluder structures (such as leaves, tree branches, and coniferous trees), and discovery and inspection of concealed artifacts on the surface. View Full-Text Keywords: computational imaging; image-based rendering; light fields; synthetic apertures
Original languageEnglish
Article number102
Number of pages12
JournalJournal of Imaging
Volume4
Issue number8
DOIs
Publication statusPublished - Aug 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Fields of science

  • 102 Computer Sciences
  • 102003 Image processing
  • 102008 Computer graphics
  • 102015 Information systems
  • 102020 Medical informatics
  • 103021 Optics

JKU Focus areas

  • Engineering and Natural Sciences (in general)

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