O. Varga, I. Nagy, P. Burai, Tamás Tomor, C. Lénárt, S. Szabó
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引用次数: 1
摘要
在我们的论文中，我们研究了基于一年时间序列数据集的ndvi描述性统计的分类机会。我们使用了2018年Sentinel-2无云图像衍生的NDVI层。根据Corine Land Cover (CLC)命名法对NDVI层进行处理，并将其分为5类。分类结果总体准确率为76.2%。在最显著错误的情况下，我们描述了分歧的原因。
Land cover analysis based on descriptive statistics of Sentinel-2 time series data
In our paper we examined the opportunities of a classification based on descriptive statistics of NDVIthroughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 imagesin 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, inaccordance with Corine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overallaccuracy. We described the reasons for the disagreement in case of the most remarkable errors. .
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