MODIS NDVI time series clustering under dynamic time warping

被引：19

作者：

Zhang, Zheng ^{[1
]}

Tang, Ping

Huo, Lianzhi

Zhou, Zengguang

机构：

[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

来源：

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING | 2014年 / 12卷 / 05期

关键词：

Time series; clustering; dynamic time warping; NDVI; MODIS; DTW barycenter averaging; similarity measure; Poyang Lake Wetlands;

D O I：

10.1142/S0219691314610116

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页数：14

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