ABSTRACT Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.

中文翻译：

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森林覆盖类型分类的快速学习方法分析

摘要 森林覆盖类型的正确映射和分类对于理解控制地表与大气相互作用机制的过程是不可或缺的。在存在大量卫星和航空测量的情况下，适当的手动分类已成为一项乏味的工作。在这项研究中，我们实施了三个不同的适度机器学习分类器以及三个统计特征选择器，以从制图变量中对不同的覆盖类型进行分类。我们的结果表明，在所选分类器中，标准随机森林分类器与主成分一起表现异常出色，不仅在整体评估中，而且在所有七个类别中。发现我们的结果明显优于涉及更复杂的深度学习模型的现有研究。