Majority Filter For Enhancing Pixel-Based Random Forest Land Cover Classification In Sukajaya District, Bogor Regency
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Majority Filter For Enhancing Pixel-Based Random Forest Land Cover Classification In Sukajaya District, Bogor Regency
Original language description
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's Accuracy (UA), Producer'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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10508 - Physical geography
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Geography, environment, sustainability
ISSN
2071-9388
e-ISSN
2542-1565
Volume of the periodical
18
Issue of the periodical within the volume
4
Country of publishing house
RU - RUSSIAN FEDERATION
Number of pages
12
Pages from-to
171-182
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105028241292