Classifier ensembles for land cover mapping using multitemporal SAR imagery

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Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “In comparing ensemble DT methods, RF generally trains quicker than boosted DTs, since boosting relies on an iterative process (Waske and Braun 2009; see also Table 7).”
A survey of multiple classifier systems as hybrid systems (2014, unknown): “[173].”
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011, methodology): “It was successfully applied to multispectral data (Pal, 2005), multitemporal SAR images (Waske and Braun, 2009), hyperspectral data (Ham et al., 2005), or multi-source data (Gislason et al., 2006), where Landsat MSS and topographic data were used.”
Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “A DT can easily be converted to a set of classification rules [16].”
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information (2014, background): “Open access under CC BY license. et al., 2012; Waske and Braun, 2009).”