Satellite Image Time Series Decomposition Based on EEMD

Remote Sensing

Yun-long Kong³  Yu Meng³  Wei Li¹  An-zhi Yue³  Yuan Yuan³  Raul Zurita-Milla²  Josef Kellndorfer² 

[1] College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; E-Mail:Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:

关键词: satellite image time series;    ensemble empirical mode decomposition;    seasonal component;    trend component;   

DOI  :  10.3390/rs71115583

来源: mdpi

【 摘 要 】

Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.