Using color-only vegetation indexes to remove vegetation from otherwise mostly mono-material point clouds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F22%3A00364333" target="_blank" >RIV/68407700:21110/22:00364333 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.46544/AMS.v27i4.20" target="_blank" >https://doi.org/10.46544/AMS.v27i4.20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.46544/AMS.v27i4.20" target="_blank" >10.46544/AMS.v27i4.20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using color-only vegetation indexes to remove vegetation from otherwise mostly mono-material point clouds

Popis výsledku v původním jazyce

Point clouds are now a standard way of describing objects in many engineering disciplines, whether they are man-made objects such as structures, buildings, or various types of structures. Commonly used methods of acquiring such data include ground, UAV, or even aerial photogrammetry, followed by terrestrial, UAV, and aerial scanning. After measurement (by the scanner) or calculation (from photogrammetry), the point cloud goes through extensive processing that essentially transforms the unordered mass of points into a usable data set. One of the important steps is removing points representing obstructing objects and features, including vegetation in particular. Here, many filtering methods based on different principles are available and suitable for application to different scenes. This paper presents a new method of filtering point clouds based on the visible spectrum color principle using vegetation indexes determined from RGB system colors only. Since each sensor has to some extent, an individual interpretation of the colors, it cannot be assumed to determine specific boundaries of what is and is no longer vegetation. Therefore, it was proposed to use means clustering to simplify the operator's work. The method was also designed in such a way that the entire evaluation could be implemented in the freely available CloudCompare software. The procedure was tested on three different sites with different terrain and vegetation characteristics showing, which demonstrated the applicability of this method to data where the color information (green) uniquely identifies vegetation. The selected vegetation filters ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily between vegetation and the rest of the point cloud without compromising the quality of the result. The method is practically implementable using the freely downloadable and usable CloudCompare software.

Název v anglickém jazyce

Using color-only vegetation indexes to remove vegetation from otherwise mostly mono-material point clouds

Popis výsledku anglicky

Point clouds are now a standard way of describing objects in many engineering disciplines, whether they are man-made objects such as structures, buildings, or various types of structures. Commonly used methods of acquiring such data include ground, UAV, or even aerial photogrammetry, followed by terrestrial, UAV, and aerial scanning. After measurement (by the scanner) or calculation (from photogrammetry), the point cloud goes through extensive processing that essentially transforms the unordered mass of points into a usable data set. One of the important steps is removing points representing obstructing objects and features, including vegetation in particular. Here, many filtering methods based on different principles are available and suitable for application to different scenes. This paper presents a new method of filtering point clouds based on the visible spectrum color principle using vegetation indexes determined from RGB system colors only. Since each sensor has to some extent, an individual interpretation of the colors, it cannot be assumed to determine specific boundaries of what is and is no longer vegetation. Therefore, it was proposed to use means clustering to simplify the operator's work. The method was also designed in such a way that the entire evaluation could be implemented in the freely available CloudCompare software. The procedure was tested on three different sites with different terrain and vegetation characteristics showing, which demonstrated the applicability of this method to data where the color information (green) uniquely identifies vegetation. The selected vegetation filters ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily between vegetation and the rest of the point cloud without compromising the quality of the result. The method is practically implementable using the freely downloadable and usable CloudCompare software.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/CK03000168" target="_blank" >CK03000168: Inteligentní metody pořizování a analýzy digitálních dat pro inspekce mostů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Acta Montanistica Slovaca

ISSN

1335-1788

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

13

Strana od-do

1089-1101

Kód UT WoS článku

000956037000014

EID výsledku v databázi Scopus

2-s2.0-85149254367