Abstract
ABSTRACTThe aim of this study is to test the performance of the Rotation Forest (RTF) algorithm in areas that have similar characteristics by using Unmanned Aerial Vehicle (UAV) images for the production of 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). The accuracy assessments showed that the RTF with 84.90% and 93.33% accuracies provided better performance than RF (7% and 4%) and GAB (15% and 11%) in urban and rural areas, respectively. Subsequently, in order to increase the classification accuracy, a majority filter was applied to post-classification images and the overall classification accuracy of the RFT was increased approximately up to 3%. Also, the results of classification were also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the classification of UAV images for land use mapping.
Highlights
Planners, scientists, resource managers and decision makers commonly use updated land use data in problem analysis aimed towards the development of environmental and life conditions (Anderson, Hardy, Roach, & Witmer, 1976; Rozenstein & Karnieli, 2011)
Rotation Forest (RTF) showed an overall classification accuracy of 84.90% in the urban area and 93.33%
When the overall accuracy of the RTF algorithm was compared with common ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB), it was observed that RTF provided better performance than RF and GAB
Summary
Scientists, resource managers and decision makers commonly use updated land use data in problem analysis aimed towards the development of environmental and life conditions (Anderson, Hardy, Roach, & Witmer, 1976; Rozenstein & Karnieli, 2011). When obtaining land use/cover data, which represent the earth surface as a map, remote-sensing technology is considered a very useful tool (Foody, 2002; Rozenstein & Karnieli, 2011; Solaimani, Arekhi, Tamartash, & Miryaghobzadeh, 2010; Sonobe, Tani, Wang, Kobayashi, & Shimamura, 2014; Zhang & Zhu, 2011). Spatial and spectral resolutions of remote-sensing data, which increased in accordance with advancing technology, led people to prefer to use them in other applications as well, such as urban and environmental applications. Image classification is the most commonly used remote-sensing technique which is used to generate land use maps by analysing high-resolution remote-sensing data
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