自动化样本生成策略用于冬季作物制图——以兰陵县为例

准确地获取作物空间分布是作物生长监测和产量预测的前提。目前，遥感图像处理需要足够的人工采集的训练样本，因此，大规模作物分布的自动获取仍然是一个挑战。以高效、经济的方式获得足够的训练样本成为作物制图的关键因素之一。因此，本文结合冬季作物物候特征与Sentinel-2时间序列影像，提出了一种自动化样本生成策略用于冬季作物制图。首先，利用归一化植被指数（NDVI）时间序列曲线进行冬季作物的判别；然后，通过时间序列曲线相似性度量的方法，判断样本点与标准的绿色叶绿素植被指数（GCVI）时间序列曲线的差距，从而为未知样本赋予正确的标签；最后，利用获取的样本训练随机森林模型，实现研究区域的冬季作物提取。最终精度评定结果：总体精度（OA）为98.46%，Kappa为0.973，表明该方法对于快速实现冬季作物自动制图的有效性。

Automated sample generation strategy for winter crop mapping: a case study in Lanling county

Accurately obtaining the spatial distribution of crops is a prerequisite for crop growth monitoring and yield prediction. At present, automatic acquisition of crop distribution is still a challenge because the processing of remote sensing image is time-consuming and the collection of sufficient training samples is laborious. How to obtain sufficient training samples in an efficient and economical way has become one of the key factors in crop mapping. By combining the phenological characteristics of winter crops with Sentinel-2 time series images, this paper proposes an automated sample generation strategy for winter crop mapping. Firstly, the normalized difference vegetation index (NDVI) time series curves are used to identify winter crops. Secondly, the differences between unknown samples and standard green chlorophyll vegetation index (GCVI) time series curves are calculated through the curve similarity measurement method, so as to assign the correct label to the unknown samples. Finally, the Random Forest model is trained with the obtained samples, which realizes the extraction of winter crops in the study area. In the final accuracy evaluation result, the overall accuracy (OA) is 98.46% and Kappa is 0.973, which shows the effectiveness of this method to realize the quick automatic winter crop mapping.