Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00367336" target="_blank" >RIV/68407700:21110/23:00367336 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/rs15133254" target="_blank" >https://doi.org/10.3390/rs15133254</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs15133254" target="_blank" >10.3390/rs15133254</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

Popis výsledku v původním jazyce

Filtering out vegetation from a point cloud based on color is only rarely used, largely due to the lack of knowledge of the suitability of input information (color, vegetation indices) and the thresholding methods. We have evaluated multiple vegetation indices (ExG, ExR, ExB, ExGr, GRVI, MGRVI, RGBVI, IKAW, VARI, CIVE, GLI, and VEG) and combined them with 10 methods of threshold determination based on training set selection (including machine learning methods) and the renowned Otsu's method. All these combinations were applied to four clouds representing vegetated rocky terrain, and the results were compared. The ExG and GLI indices were generally the most suitable for this purpose, with the best F-scores of 97.7 and 95.4, respectively, and the best-balanced accuracies for the same combination of the method/vegetation index of 98.9 and 98.3%, respectively. Surprisingly, these best results were achieved using the simplest method of threshold determination, considering only a single class (vegetation) with a normal distribution. This algorithm outperformed all other methods, including those based on a support vector machine and a deep neural network. Thanks to its simplicity and ease of use (only several patches representing vegetation must be manually selected as a training set), this method can be recommended for vegetation removal from rocky and anthropogenic surfaces.

Název v anglickém jazyce

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

Popis výsledku anglicky

Filtering out vegetation from a point cloud based on color is only rarely used, largely due to the lack of knowledge of the suitability of input information (color, vegetation indices) and the thresholding methods. We have evaluated multiple vegetation indices (ExG, ExR, ExB, ExGr, GRVI, MGRVI, RGBVI, IKAW, VARI, CIVE, GLI, and VEG) and combined them with 10 methods of threshold determination based on training set selection (including machine learning methods) and the renowned Otsu's method. All these combinations were applied to four clouds representing vegetated rocky terrain, and the results were compared. The ExG and GLI indices were generally the most suitable for this purpose, with the best F-scores of 97.7 and 95.4, respectively, and the best-balanced accuracies for the same combination of the method/vegetation index of 98.9 and 98.3%, respectively. Surprisingly, these best results were achieved using the simplest method of threshold determination, considering only a single class (vegetation) with a normal distribution. This algorithm outperformed all other methods, including those based on a support vector machine and a deep neural network. Thanks to its simplicity and ease of use (only several patches representing vegetation must be manually selected as a training set), this method can be recommended for vegetation removal from rocky and anthropogenic surfaces.

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

2023

Kód důvěrnosti údajů

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

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

24

Strana od-do

1-24

Kód UT WoS článku

001030870000001

EID výsledku v databázi Scopus

2-s2.0-85165167453