| Title: | Reflection symmetry detection in earth observation data |
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| Authors: | ID Podgorelec, David (Author)
ID Lukač, Luka (Author)
ID Žalik, Borut (Author) |
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| Files: |  Podgorelec-2023-Reflection_Symmetry_Detection.pdf (8,92 MB)
MD5: 19A16E2D15526404416FA82D7B8ED256
 https://doi.org/10.3390/s23177426
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | The paper presents a new algorithm for reflection symmetry detection, which is specialized to detect maximal symmetric patterns in an Earth observation (EO) dataset. First, we stress the particularities that make symmetry detection in EO data different from detection in other geometric sets. The EO data acquisition cannot provide exact pairs of symmetric elements and, therefore, the approximate symmetry must be addressed, which is accomplished by voxelization. Besides this, the EO data symmetric patterns in the top view usually contain the most useful information for further processing and, thus, it suffices to detect symmetries with vertical symmetry planes. The algorithm first extracts the so-called interesting voxels and then finds symmetric pairs of line segments, separately for each horizontal voxel slice. The results with the same symmetry plane are then merged, first in individual slices and then through all the slices. The detected maximal symmetric patterns represent the so-called partial symmetries, which can be further processed to identify global and local symmetries. LiDAR datasets of six urban and natural attractions in Slovenia of different scales and in different voxel resolutions were analyzed in this paper, demonstrating high detection speed and quality of solutions. |
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| Keywords: | computer science, approximate symmetry, partial symmetry, local symmetry, point cloud, voxel, line segment |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 19.07.2023 |
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| Article acceptance date: | 23.08.2023 |
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| Publication date: | 25.08.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | Str. 1-17 |
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| Numbering: | Letn. 23, Št. 17, št. članka 7426 |
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| PID: | 20.500.12556/DKUM-86036  |
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| UDC: | 681.5 |
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| ISSN on article: | 1424-8220 |
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| COBISS.SI-ID: | 162734851 |
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| DOI: | 10.3390/s23177426 |
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| Copyright: | © 2023 by the authors |
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| Publication date in DKUM: | 28.09.2023 |
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| Views: | 597 |
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| Downloads: | 47 |
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| Metadata: |    |
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| Categories: | Misc.
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