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Multi-Temporal Indices Derived from Time Series of Sentinel-1 Images as a Phenological Description of Plants Growing for Crop Classification

机译:从Sentinel-1图像的时间序列得出的多时相指标,作为植物生长的作物分类的物候学描述

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Agricultural land cover is characterized by fast changes within time. The phenological dynamic of plants can deliver crucial information for crop classifications. To enhance a crop classification multi-temporal indices are proposed. They are calculated based on the time series of coherence matrices and parameters of H/α decomposition derived from dual polarimetric synthetic aperture radar images (Sentinel-1). The study shows that the use of multi-temporal indices increases the accuracy of crop classification by 9% in comparison to classification based only on the time series of coherence matrices.
机译:农业用地覆盖的特点是随时间变化迅速。植物的物候动态可以为作物分类提供关键信息。为了增强作物分类,提出了多时相指数。它们是根据相干矩阵的时间序列和从双极化合成孔径雷达图像(Sentinel-1)得出的H /α分解参数计算得出的。研究表明,与仅基于相干矩阵的时间序列进行分类相比,使用多时相索引可使作物分类的准确性提高9%。

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