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Reflection Symmetry Detection in Earth Observation Data.

David Podgorelec1, Luka Lukač1, Borut Žalik1

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm for detecting approximate reflection symmetry in Earth observation (EO) data. The method efficiently identifies maximal symmetric patterns, crucial for analyzing complex geospatial information.

Keywords:
approximate symmetrycomputer scienceline segmentlocal symmetrypartial symmetrypoint cloudvoxel

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Area of Science:

  • Geospatial analysis
  • Computer vision
  • Pattern recognition

Background:

  • Symmetry detection in Earth observation (EO) data presents unique challenges compared to other geometric datasets.
  • EO data acquisition necessitates addressing approximate symmetry due to inherent inaccuracies.
  • Vertical symmetry planes are often most informative for top-view analysis of EO data.

Purpose of the Study:

  • To develop a specialized algorithm for detecting maximal reflection symmetry patterns in Earth observation datasets.
  • To address the specific challenges of approximate symmetry detection in voxelized EO data.
  • To enable further processing of detected symmetries for identifying global and local patterns.

Main Methods:

  • Voxelization is employed to handle approximate symmetry in EO data.
  • The algorithm identifies interesting voxels and then detects symmetric pairs of line segments within horizontal voxel slices.
  • Symmetry results are merged across individual slices and then across all slices to identify maximal symmetric patterns.

Main Results:

  • The algorithm successfully detects maximal symmetric patterns, referred to as partial symmetries.
  • Analysis of LiDAR datasets from diverse Slovenian locations demonstrated high detection speed.
  • The method proved effective across various scales and voxel resolutions, showcasing quality solutions.

Conclusions:

  • The developed algorithm offers an efficient and accurate solution for reflection symmetry detection in EO data.
  • The identified partial symmetries serve as a foundation for detecting both global and local symmetries.
  • The approach is validated by its performance on real-world LiDAR datasets.