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Remote Sens. 2015, 7(11), 15583-15604; doi:10.3390/rs71115583

Satellite Image Time Series Decomposition Based on EEMD

1,2
,
1,* , 3,†
,
1,†
and
1,2,†
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Raul Zurita-Milla, Josef Kellndorfer and Prasad Thenkabail
Received: 15 June 2015 / Revised: 29 October 2015 / Accepted: 12 November 2015 / Published: 19 November 2015

Abstract

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. View Full-Text
Keywords: satellite image time series; ensemble empirical mode decomposition; seasonal component; trend component satellite image time series; ensemble empirical mode decomposition; seasonal component; trend component
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kong, Y.-L.; Meng, Y.; Li, W.; Yue, A.-Z.; Yuan, Y. Satellite Image Time Series Decomposition Based on EEMD. Remote Sens. 2015, 7, 15583-15604.

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