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

By Phan Thanh Noi, Martin Kappas. Italian National Conference on Sensors, 2017

1,229 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/10ce4e6f5369e70cf903d2f27bd766040ecfee66

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Citations in the corpus

Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “Another research performed a comparative study between RF, kNN, and SVM to classify six different classes (including agricultural landscape) by using images taken through Sentinel-2 satellite (Thanh Noi & Kappas, 2018).”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “The accuracy of a classification varied with methods, techniques, time and space [41,52,93,94].”
Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications (2020, background): “In general terms, the combination of Sentinel-2 imagery and various algorithmic approaches, such as random forest, k-nearest neighbor, and support vector machine have also shown optimal classification accuracy, resulting with over 90% of overall accuracy for the various approaches [54].”