Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine).

中文翻译：

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开发基于对象的土地覆被分类规则集的决策树算法

决策树（DT）算法是用于土地覆被分类的重要非参数工具。尽管将不同的DT应用于Landsat土地覆盖分类，但尚未比较它们的各个分类精度和性能，尤其是在为制定基于对象的土地覆盖分类的规则集而产生准确阈值的有效性方面。在这里，重点是比较五种DT算法的性能：Tree，C5.0，Rpart，Ipred和Party。这些DT算法用于使用赞比亚铜带省的Landsat 8影像对十种土地覆盖类别进行分类。通过开发由DT定义的阈值的规则集，使用基于对象的图像分析（OBIA）进行分类。DT算法的性能基于以下评估：（1）通过交叉验证的DT准确性；（2）专题图的土地覆被分类精度；（3）其他结构属性，例如树形图的大小和变量选择能力。结果表明，只有从具有简单结构和最少变量的DT算法开发的规则集，才能产生较高的土地覆被分类精度（总体准确性> 88％）。因此，与C5.0和Party DT算法相比，诸如Tree和Rpart之类的算法产生了更高的分类结果，而C5.0和Party DT算法涉及很多分类变量。这种高度的准确性归因于在Tree和Rpart DT训练期间能够最大程度地减少过度拟合的能力以及处理数据中噪声的能力。该研究对用于OBIA规则集开发的DT算法的形式选择产生了新见解。因此，Tree和Rpart算法可用于开发规则集，因为它们产生较高的土地覆被分类精度并具有简单的结构。作为未来研究的途径，可以将DT算法的性能与现代机器学习分类器（例如，Random Forest和Support Vector Machine）进行比较。