Abstract. The use of remote sensing data has become very useful to generate statistical information about society and its environment. In this sense, land use and land cover classification (LULC) are tasks related to determining the cover on the Earth’s surface. In the decision-making process, this kind of information is relevant to handle in the best way how the information about events such as earthquakes or cadastre information can be used. To attend this, a methodology to perform supervised classification based on the combination of statistical, textural, and shape features for the LULC classification problem is proposed. For the experiments, thousands of Landsat and Sentinel images covering all of Mexico and Europe, respectively, were used. Twelve LULC classes were established for Mexico territory, and 10 were established for Europe. This methodology was applied to both datasets and benchmarking with multiple well-known classifiers (random forest, support vector machines, extra trees, and artificial neural network). As a result, the overall accuracy (OA) for Landsat and Sentinel-2 was reached 77.1% and 96.7%, respectively. The performance of the different types of features was compared using Mexico and Europe images. Interesting results were achieved and some conclusions about using traditional and non-traditional features were found in the LULC classification task.

中文翻译：

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基于 Landsat 和 Sentinel 影像的统计、纹理和形状特征的土地利用和土地覆盖分类特征评估

摘要。遥感数据的使用对于生成关于社会及其环境的统计信息非常有用。从这个意义上说，土地利用和土地覆被分类 (LULC) 是与确定地球表面覆被相关的任务。在决策过程中，这类信息关系到以最佳方式处理地震等事件信息或地籍信息如何使用。为此，提出了一种基于统计、纹理和形状特征组合的监督分类方法，用于解决 LULC 分类问题。在实验中，使用了分别覆盖整个墨西哥和欧洲的数千张 Landsat 和 Sentinel 图像。为墨西哥领土建立了 12 个 LULC 类，为欧洲建立了 10 个类。这种方法适用于数据集和多个知名分类器（随机森林、支持向量机、额外树和人工神经网络）的基准测试。结果，Landsat 和 Sentinel-2 的整体精度 (OA) 分别达到了 77.1% 和 96.7%。使用墨西哥和欧洲图像比较了不同类型特征的性能。取得了有趣的结果，在 LULC 分类任务中发现了一些关于使用传统和非传统特征的结论。使用墨西哥和欧洲图像比较了不同类型特征的性能。取得了有趣的结果，在 LULC 分类任务中发现了一些关于使用传统和非传统特征的结论。使用墨西哥和欧洲图像比较了不同类型特征的性能。取得了有趣的结果，在 LULC 分类任务中发现了一些关于使用传统和非传统特征的结论。