Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site

By Georgios Mallinis, Nikos Koutsias, M. Tsakiri-Strati, M. Karteris. 2008

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A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…Hofmann et al., 2011; Kim et al., 2011; Leon and Woodroffe, 2011; Li et al., 2014; Li and Shao, 2013; Lisita et al., 2013; Macfaden et al., 2012; Mallinis et al., 2008; Mishra and Crews, 2014; Moskal et al., 2011; Myint et al., 2011; Phinn et al., 2012; Tzotsos et al., 2011; Walker and…”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “For example, k -NN only requires setting the k parameter (Yu et al. 2006; Mallinis et al. 2008), and the Adaboost implementation of boosted DT only require the number of trees in the ensemble (Freund and Schapire 1996).”
Object based image analysis for remote sensing (2010, background): “Mallinis et al. (2008) performed a multi-scale, object based analysis of a QuickBird satellite image to delineate forest vegetation polygons in a natural forest in Northern Greece.”
A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification (2016, background): “The adoption of objects instead of pixels provided much more information and challenges for classification [12].”