POINT CLOUD LOCAL NEIGHBORHOOD FEATURES USED FOR CLASSIFICATION-A REVIEW

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00383694" target="_blank" >RIV/68407700:21110/25:00383694 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/CEJ.2025.01.0005" target="_blank" >https://doi.org/10.14311/CEJ.2025.01.0005</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/CEJ.2025.01.0005" target="_blank" >10.14311/CEJ.2025.01.0005</a>

Result language
angličtina
Original language name
POINT CLOUD LOCAL NEIGHBORHOOD FEATURES USED FOR CLASSIFICATION-A REVIEW
Original language description
Point clouds are essential for 3D spatial analysis and widely used in geodesy, photogrammetry, and remote sensing. While modern technologies simplify their collection, processing remains challenging due to data size, irregularity, and noise. Classification is critical for object identification and noise removal. This paper explores geometric features of points derived from their local 3D neighbourhoods. It examines neighbourhood definitions, feature computation via principal component analysis (PCA), and their impact on real dataset classification. Using a test point cloud with natural and anthropogenic features, we analyze feature dependencies, identify redundancies, and highlight key metrics. Additionally, we propose new approaches for noise filtering, contributing to more efficient point cloud processing and practical applications.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20101 - Civil engineering

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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