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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/143332026-10-18T20:33:49Zopenairedrivercom_ebyu_10000col_ebyu_10001

The Rotation Forest algorithm and object-based classification method for land use mapping through UAV imagesAkar, ÖzlemclassificationMcNemar testRandom ForestRotation Forestunmanned aerial vehicleaccuracy assessmentaerial photographalgorithmcomparative studyGISland useunmanned vehiclevegetation mappingThis study aims to test the performance of the Rotation Forest (RTF) algorithm in urban and rural areas that have similar characteristics using unmanned aerial vehicle (UAV) images to produce the most up-to-date and accurate land use maps. The performance of the RTF algorithm was compared to other ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB) for object-based classification. RGB bands and other variables (i.e. ratio, mean, standard deviation,.. etc.) were also used in classification. The accuracy assessments showed that the RTF method, with 92.52 and 91.29% accuracies, performed better than the RF (2 and 4%) and GAB (5 and 8%) methods in urban and rural areas, respectively. The significance of differences in classification methods was also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the object-based classification of UAV images for land use mapping. © 2017 Informa UK Limited, trading as Taylor & Francis Group.Taylor and Francis Ltd.2018Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/14333https://doi.org/10.1080/10106049.2016.12772731010-6049enGeocarto Internationalinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/14333