Satellite Image Time Series Decomposition Based on EEMD
Abstract
:1. Introduction
2. Proposed Framework
2.1. Ensemble Empirical Mode Decomposition
2.2. EEMD for SITS
2.2.1. Seasonal Component Extraction
2.2.2. Trend Component Extraction
2.2.3. Trend Component for Change Detection
3. Experiment and Discussion
3.1. 16-Day Composite NDVI Time Series of Forest Type
3.1.1. Seasonal-Trend Decomposition
3.1.2. Validation of the Seasonal Component
3.2. Sixteen-Day Composition GEMI Time Series with Disturbance
IMF1 | IMF2 | IMF3 | IMF4 | IMF5 | IMF6 | IMF7 | Residue | |
---|---|---|---|---|---|---|---|---|
Energy | 0.878 | 2.606 | 6.860 | 1.522 | 0.370 | 0.002 | 0.212 | 0.760 |
4. Conclusions
- (1)
- Further algorithm improvement should consider the sensitivity of change point detection of the seasonal or trending component.
- (2)
- Further research is necessary to incrementally update an event database when new observations are available and to enhance the prediction algorithm to achieve real-time change detection (see Schnebele, et al. [35]).
Acknowledgments
Author Contributions
Conflicts of Interest
References
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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. https://doi.org/10.3390/rs71115583
Kong Y-l, Meng Y, Li W, Yue A-z, Yuan Y. Satellite Image Time Series Decomposition Based on EEMD. Remote Sensing. 2015; 7(11):15583-15604. https://doi.org/10.3390/rs71115583
Chicago/Turabian StyleKong, Yun-long, Yu Meng, Wei Li, An-zhi Yue, and Yuan Yuan. 2015. "Satellite Image Time Series Decomposition Based on EEMD" Remote Sensing 7, no. 11: 15583-15604. https://doi.org/10.3390/rs71115583