POINT CLOUD LOCAL NEIGHBORHOOD FEATURES USED FOR CLASSIFICATION-A REVIEW

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

POINT CLOUD LOCAL NEIGHBORHOOD FEATURES USED FOR CLASSIFICATION-A REVIEW

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

POINT CLOUD LOCAL NEIGHBORHOOD FEATURES USED FOR CLASSIFICATION-A REVIEW

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The Civil Engineering Journal

ISSN

1210-4027

e-ISSN

1805-2576

Svazek periodika

34

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

64-79

Kód UT WoS článku

001491923800005

EID výsledku v databázi Scopus

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