{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T02:42:00Z","timestamp":1782441720320,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T00:00:00Z","timestamp":1513900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use\/cover classification using Sentinel-2 image data. An area of 30 \u00d7 30 km2 within the Red River Delta of Vietnam with six land use\/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels\/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels\/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.<\/jats:p>","DOI":"10.3390\/s18010018","type":"journal-article","created":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T05:50:19Z","timestamp":1513921819000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":967,"title":["Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"18","author":[{"given":"Phan","family":"Thanh Noi","sequence":"first","affiliation":[{"name":"Cartography, GIS and Remote Sensing Department, Institute of Geography, University of G\u00f6ttingen, Goldschmidt Street 5, 37077 G\u00f6ttingen, Germany"},{"name":"Cartography and Geodesy Department, Land Management Faculty, Vietnam National University of Agriculture, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3173-4870","authenticated-orcid":false,"given":"Martin","family":"Kappas","sequence":"additional","affiliation":[{"name":"Cartography, GIS and Remote Sensing Department, Institute of Geography, University of G\u00f6ttingen, Goldschmidt Street 5, 37077 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1890\/1540-9295(2004)002[0249:LCBHNA]2.0.CO;2","article-title":"Land-use choices: Balancing human needs and ecosystem function","volume":"2","author":"DeFries","year":"2004","journal-title":"Front. 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