Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

421 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery (2017, methodology): “Therefore, among the nonparametric methods, SVM and RF are becoming increasingly popular in image classification studies [34].”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “Pal (2005) and Adam et al. (2014) found that SVM and RF performed equally well in terms of accuracy, whereas Zhang and Xie (2013) and Maxwell et al. (2014a, 2014b, 2015) found that SVM outperformed RF.”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, methodology): “However, the differences in area under LULC classes of different classifiers occur due to the differences in the parameter optimization of the models, techniques and the accuracy differences in the algorithms used [42,92].”
Remote sensing for wetland classification: a comprehensive review (2018, background): “Several studies, including Duro, Franklin, and Dubé (2012), Qian et al. (2014), and Adam et al. (2014), have evaluated the accuracy of the SVM classifier for wetland classification.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “, random forest (RF) [10], and deep learning algorithms [27].”