Majority Filter For Enhancing Pixel-Based Random Forest Land Cover Classification In Sukajaya District, Bogor Regency

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F25%3A10514007" target="_blank" >RIV/00216208:11310/25:10514007 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ra_nyUyIH8" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ra_nyUyIH8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24057/2071-9388-2025-4183" target="_blank" >10.24057/2071-9388-2025-4183</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Majority Filter For Enhancing Pixel-Based Random Forest Land Cover Classification In Sukajaya District, Bogor Regency

Popis výsledku v původním jazyce

High-quality land cover data are essential for environmental policy, spatial planning, and ecosystem monitoring. However, pixel-based classification methods, while widely used due to their practicality, often suffer from salt-and-pepper noise, which undermines map reliability. This study aimed to integrate Random Forest (RF) classification and majority filtering to enhance the quality of land cover mapping in Sukajaya District, Bogor Regency. RF was applied to Sentinel-2 image data with varying numbers of trees (ntree) to determine the optimal model performance. Subsequently, majority filtering was applied to each classification result to reduce noise and improve spatial coherence. The evaluation employed multiple accuracy metrics, including User&apos;s Accuracy (UA), Producer&apos;s Accuracy (PA), F1-Score, Overall Accuracy (OA), and Kappa Coefficient (KC). Comprehensive accuracy increased with the ntree until reaching an optimal point. Beyond this point, additional ntree resulted in diminishing returns. Applying majority filtering as a post-processing procedure led to further improvements in classification accuracy. While majority filtering can reduce classification noise and improve the visual quality of land cover maps, it also carries the risk of removing small, accurately classified land cover patches. This consequence is rarely discussed in similar studies. These findings highlight the importance of integrating pixel-based machine learning classification with majority filtering in land cover classification workflows, while emphasising a trade-off that tends to favour visual accuracy over the preservation of spatial detail.

Název v anglickém jazyce

Majority Filter For Enhancing Pixel-Based Random Forest Land Cover Classification In Sukajaya District, Bogor Regency

Popis výsledku anglicky

High-quality land cover data are essential for environmental policy, spatial planning, and ecosystem monitoring. However, pixel-based classification methods, while widely used due to their practicality, often suffer from salt-and-pepper noise, which undermines map reliability. This study aimed to integrate Random Forest (RF) classification and majority filtering to enhance the quality of land cover mapping in Sukajaya District, Bogor Regency. RF was applied to Sentinel-2 image data with varying numbers of trees (ntree) to determine the optimal model performance. Subsequently, majority filtering was applied to each classification result to reduce noise and improve spatial coherence. The evaluation employed multiple accuracy metrics, including User&apos;s Accuracy (UA), Producer&apos;s Accuracy (PA), F1-Score, Overall Accuracy (OA), and Kappa Coefficient (KC). Comprehensive accuracy increased with the ntree until reaching an optimal point. Beyond this point, additional ntree resulted in diminishing returns. Applying majority filtering as a post-processing procedure led to further improvements in classification accuracy. While majority filtering can reduce classification noise and improve the visual quality of land cover maps, it also carries the risk of removing small, accurately classified land cover patches. This consequence is rarely discussed in similar studies. These findings highlight the importance of integrating pixel-based machine learning classification with majority filtering in land cover classification workflows, while emphasising a trade-off that tends to favour visual accuracy over the preservation of spatial detail.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2025

Kód důvěrnosti údajů

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

Název periodika

Geography, environment, sustainability

ISSN

2071-9388

e-ISSN

2542-1565

Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

RU - Ruská federace

Počet stran výsledku

12

Strana od-do

171-182

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105028241292