MODIS NDVI time series clustering under dynamic time warping

Abstract

For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.