ABSTRACT Reliable information of croplands has useful implications for agriculture. Based on random forest classifier, a cropland extraction method was developed. Multi-temporal image data of Landsat 8 were used for classifying crop and non-crop vegetation, since these data contain useful information. However, there is also large redundancy. To solve this problem, a new feature selection method was proposed in this paper. The primary innovativeness resides in a temporal feature selection criterion. A classification experiment was carried out to validate the proposed technique. The results showed that the proposed method can decrease feature redundancy with little cost on classification performance.

中文翻译：

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基于随机森林和时间特征选择的内蒙古西部耕地提取

摘要 可靠的农田信息对农业具有重要意义。提出了一种基于随机森林分类器的农田提取方法。Landsat 8 的多时相图像数据用于对作物和非作物植被进行分类，因为这些数据包含有用的信息。但是，也有很大的冗余。为了解决这个问题，本文提出了一种新的特征选择方法。主要创新在于时间特征选择标准。进行了分类实验以验证所提出的技术。结果表明，所提出的方法可以减少特征冗余，而对分类性能的影响很小。