Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

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ID: 118817
2017
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Abstract
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 × 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.
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noi2017sensorscomparison Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Phan Thanh Noi;Martin Kappas;Thanh Noi, Phan;Kappas, Martin;
Journal sensors
Year 2017
DOI
10.3390/s18010018
URL
https://www.mdpi.com/1424-8220/18/1/18#viewedby
Keywords
Classification Algorithms sentinel-2 support vector machine (svm) random forest (rf) k-nearest neighbor (knn) training sample size National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:29271909 pmc5796274 doi:10.3390/s18010018 phan thanh noi martin kappas

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