Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms

By John Richard Otukei, Thomas Blaschke. International Journal of Applied Earth Observation and Geoinformation, 2010

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “11 MLC, SVM, DT DT Kibale Sub-county, Eastern Uganda [103]”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, methodology): “…has been widely explored in LULC classification using commonly used multi-spectral imagery (Cingolani et al. 2004; Erbek, Özkan, and Taberner 2004; Otukei and Blaschke 2010; Ustin et al. 1996; Gislason, Benediktsson, and Sveinsson 2006; Rodriguez-Galiano et al. 2012; Duro, Franklin, and Dubé…”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “Otukei and Blaschke (2010) presented a method using DM to provide a decision threshold and argued that this is a reliable, transferable and reproducible mechanism which addresses the inconsistency inherent to a human.”
Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures (2018, methodology): “Many studies have, however, shown that the classification accuracy level of the SVM method outperformed the MLC method (Otukei and Blaschke, 2010; Poursanidis et al., 2015).”