Abstract

The 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.

Details

Title
Mapping land use with using Rotation Forest algorithm from UAV images
Author
Akar, Özlem 1   VIAFID ORCID Logo 

1  Vocational School Land Registry and Cadastre, Erzincan University, Erzincan, Turkey 
End page
279
Publication year
2017
Publication date
Nov 2017
Publisher
Taylor & Francis Ltd.
e-ISSN
22797254
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1080/22797254.2017.1319252
ProQuest document ID
2200896214
Copyright
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.