ABSTRACT

Inter-annual Normalized Difference Vegetation Index (NDVI) time series has been applied to change detection in many fields. Research on change detection in inter-annual NDVI time series always requires several related parameters or sample training, which may limit the transferability of the methods to some extent. In this letter, we try to develop a shape-based clustering to analyse the inter-annual NDVI time series by considering the shapes of time series. The method adopts shape-based distance based on cross-correlation analysis to measure the distance between time series and uses shape-based averaging method named Dynamic Time Warping Barycentre Averaging to get the cluster centroids. Through experimental analysis, the shape-based clustering can well gather the NDVI time series with similar trends into a cluster. Also, the shape-based clustering is proven to be reliable with 94.4% overall accuracy for detection of vegetation disturbance.

摘要

年际归一化植被指数 (NDVI) 时间序列已应用于许多领域的变化检测。对年际 NDVI 时间序列变化检测的研究总是需要几个相关的参数或样本训练，这可能在一定程度上限制了方法的可移植性。在这封信中，我们尝试开发一种基于形状的聚类，通过考虑时间序列的形状来分析年际 NDVI 时间序列。该方法采用基于互相关分析的基于形状的距离来测量时间序列之间的距离，并使用称为动态时间扭曲重心平均的基于形状的平均方法来获得聚类质心。通过实验分析，基于形状的聚类可以很好地将趋势相似的NDVI时间序列聚集成一个聚类。还，