Abstract

All the supervised classification methods need sufficient and efficient samples, which are commonly labeled by visual inspection. In this study, to resolve the issues of insufficient training samples and time-consuming, a novel method for detailed and automated LULC classification by LC_Type1 of MCD12Q1 IGBP schemes in the GEE cloud platform was proposed based on the RF and CART classifiers. The results present that the validation overall accuracy of the RF classifier is higher than the CART, 87.24% in Australia, and 85.18% in the USA, respectively. The automated classification results of the RF classifier are more concentrated than CART, which the RF classifier is more suitable for this automated method. Moreover, the proposed method can accomplish accurate, detailed, and automated LULC classification based on the GEE which is making satellite imagery computing an efficient, flexible, and fast process. The workflow provides a reliable method for detailed, automated, and remotely LULC classification.

摘要

所有有监督的分类方法都需要足够有效的样本，这些样本通常通过视觉检查来标记。在本研究中，针对训练样本不足和耗时的问题，提出了一种基于RF和CART分类器的GEE云平台MCD12Q1 IGBP方案的LC_Type1进行详细自动化LULC分类的新方法。结果表明，RF分类器的验证总体准确率高于CART，分别在澳大利亚和美国分别为87.24%和85.18%。RF分类器的自动化分类结果比CART更集中，RF分类器更适合这种自动化方法。此外，所提出的方法可以实现准确、详细、基于 GEE 的自动 LULC 分类使卫星图像计算成为一个高效、灵活和快速的过程。该工作流程为详细、自动化和远程 LULC 分类提供了一种可靠的方法。