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首页> 外文期刊>Journal of Science and Technology of Agriculture and Natural Resources >Performance Evaluation of Three Image Classification Methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in Land Use Mapping
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Performance Evaluation of Three Image Classification Methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in Land Use Mapping

机译：土地利用制图中三种图像分类方法（随机森林，支持向量机和最大似然）的性能评估

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摘要

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.
机译：土地使用/覆盖图是大多数环境模拟模型的基本输入;因此，从卫星图像分类得出的地图的准确性降低了建模的不确定性。这项研究的目的是评估基于分类方法（随机森林和支持向量机）的机器学习生成的地图的准确性，并将其与常见的分类方法（最大似然）进行比较。为此，在初始校正之后，使用了研究区域（阿塞拜疆东部的萨塔克汉大坝盆地）Landsat 8的OLI传感器的图像。考虑了五种土地用途，包括城市，灌溉和雨养农业，范围和水体。为了进行监督分类，在两组教育（占总数的70％）和测试（占30％）数据中使用了地面真实数据。使用了准确度指标，并使用McNemar检验显示了方法性能之间的显着统计差异。结果表明，支持向量机，随机森林法和最大似然法的总体准确性分别为96.6％，90.8％和90.8％。这些方法的卡伯系数分别为0.93、0.81和0.83。支持向量机算法与其他两种算法的性能在95％置信度之间存在显着的统计差异，这已通过McNemar测试确认。

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