CHANGE DETECTION WORKFLOW FOR MAPPING CHANGES FROM ARABLE LANDS TO PERMANENT GRASSLANDS WITH ADVANCED BOOSTING METHODS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F19%3A10400287" target="_blank" >RIV/00216208:11310/19:10400287 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=gg3OIM9zCe" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=gg3OIM9zCe</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.379-394" target="_blank" >10.15292/geodetski-vestnik.2019.03.379-394</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CHANGE DETECTION WORKFLOW FOR MAPPING CHANGES FROM ARABLE LANDS TO PERMANENT GRASSLANDS WITH ADVANCED BOOSTING METHODS

Popis výsledku v původním jazyce

The necessity of mapping changes in land cover categories based on satellite imageries is a challenging task especially in terms of arable land and grasslands. The phenological phases of arable lands change quickly while grasslands is more stable. It might be hard to capture these changes regarding the spectral overlap between crops in full growth and grass itself. We have introduced a relatively simple processing workflow with good efficiency and accuracy. Our proposed method utilises the combination of a Multivariate Alteration Change Detection Algorithm and an existing boosting method, such as the AdaBoost algorithm with different weak learners and the most recent one - Extreme Gradient Boosting that is actually a relatively new approach in remote sensing. According to the results, the highest overall accuracy is 89.51 %. The proposed process workflow was tested on Landsat data with 30 m spatial resolution, using opensource software: R and GRASS GIS, Orfeo Toolbox library.

Název v anglickém jazyce

CHANGE DETECTION WORKFLOW FOR MAPPING CHANGES FROM ARABLE LANDS TO PERMANENT GRASSLANDS WITH ADVANCED BOOSTING METHODS

Popis výsledku anglicky

The necessity of mapping changes in land cover categories based on satellite imageries is a challenging task especially in terms of arable land and grasslands. The phenological phases of arable lands change quickly while grasslands is more stable. It might be hard to capture these changes regarding the spectral overlap between crops in full growth and grass itself. We have introduced a relatively simple processing workflow with good efficiency and accuracy. Our proposed method utilises the combination of a Multivariate Alteration Change Detection Algorithm and an existing boosting method, such as the AdaBoost algorithm with different weak learners and the most recent one - Extreme Gradient Boosting that is actually a relatively new approach in remote sensing. According to the results, the highest overall accuracy is 89.51 %. The proposed process workflow was tested on Landsat data with 30 m spatial resolution, using opensource software: R and GRASS GIS, Orfeo Toolbox library.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Geodetski Vestnik

ISSN

0351-0271

e-ISSN

—

Svazek periodika

63

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

SI - Slovinská republika

Počet stran výsledku

16

Strana od-do

379-394

Kód UT WoS článku

000488291900007

EID výsledku v databázi Scopus

2-s2.0-85073748675