Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China

By Gao Yan, Jean-François Mas, B. Maathuis, Xiang Zhang, P. M. V. Dijk. 2006

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Comparing object-based and pixel-based classifications for mapping savannas (2011, background): “This is consistent with findngs within the literature (Castillejo-Gonzalez et al., 2009; Gao and as, 2008; Gao et al., 2006).”
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery (2014, result): “In addition, previous comparison studies of machine learning classifiers have been mostly focused on non-urban areas, such as grasslands, farmlands and coal mine area [5,9,20].”
Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Various land cover classification approaches using spectral and spatial information have been intensively studied (Blaschke, 2010; Duro et al., 2012; Foody, 1995; Hu et al., 2018a; Myint et al., 2011; Yan et al., 2006).”
Object based image analysis for remote sensing (2010, background): “Yan et al. (2006) compared per-pixel and OBIA classifications for land-cover mapping in a coal fire area in Inner Mongolia, and found the differences in accuracy, expressed in terms of proportions of correctly allocated pixels, to be statistically significant.”