Volume 22, Issue 4 (Winter 2019)                   jwss 2019, 22(4): 235-247 | Back to browse issues page

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Jahanbakhshi F, Ekhtesasi M R. Performance Evaluation of Three Image Classification Methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in Land Use Mapping. jwss 2019; 22 (4) :235-247
URL: http://jstnar.iut.ac.ir/article-1-3610-en.html
1. Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran. , mr_ekhtesasi@yahoo.com
Abstract:   (6175 Views)
Land use/cover maps are the basic inputs for most of the environmental simulation models; hence, the accuracy of the maps derived from the classification of the satellite images reduces the uncertainty in modeling. The aim of this study was to assess the accuracy of the maps produced by machine learning based on classification methods (Random Forest and Support Vector Machine) and to compare them with a common classification method (Maximum Likelihood). For this purpose, the image of the OLI sensor of Landsat 8 for the study area (Sattarkhan Dam’s basin in the Eastern Azerbaijan) was used after the initial corrections. Five land uses including urban, irrigated and rain-fed agriculture, range and water body were considered. For conducting the supervised classification, ground truth data were used in two sets of educational (70% of the total) and test (30%) data. Accuracy indexes were used and the McNemar test was employed to show the significant statistical difference between the performances of the methods. The results indicates that the overall accuracy of Support Vector Machine, Random Forest, and Maximum Likelihood methods was 96.6, 90.8, and 90.8 %, respectively; also the Kappa coefficient for these methods was 0.93, 0.81 and 0.83, respectively. The existence of a significant statistical difference at the 95% confidence between the performances of the Support Vector Machine algorithm and the other two algorithms was confirmed by the McNemar test.
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Type of Study: Research | Subject: Ggeneral
Received: 2017/11/6 | Accepted: 2018/02/24 | Published: 2019/03/15

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