This study aims to test the performance of the Rotation Forest (RTF) algorithm in urban and rural areas that have similar characteristics by 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 were 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.
Keywords: Rotation Forest, Random Forest, Unmanned Aerial Vehicle, Classification, McNemar test