Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00350862" target="_blank" >RIV/68407700:21110/21:00350862 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/rs13153050" target="_blank" >https://doi.org/10.3390/rs13153050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs13153050" target="_blank" >10.3390/rs13153050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Popis výsledku v původním jazyce

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAracterisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6-60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

Název v anglickém jazyce

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Popis výsledku anglicky

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAracterisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6-60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

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

2072-4292

Svazek periodika

13

Číslo periodika v rámci svazku

15

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000682158900001

EID výsledku v databázi Scopus

2-s2.0-85112114098