Lack of the global knowledge of land-cover changes limits our understanding of the earth system, hinders natural resource management and also compounds risks. Remote sensing data provides an opportunity to automatically detect and monitor land-cover changes. Although changes in land cover can be observed from remote sensing time series, most traditional change point detection algorithms do not perform well due to the unique properties of the remote sensing data, such as noise, missing values and seasonality. We propose an online change point detection method that addresses these challenges. Using an independent validation set, we show that the proposed method performs better than the four baseline methods in both of the two testing regions, which has ecologically diverse features.
|Original language||English (US)|
|Title of host publication||Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Editors||Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|State||Published - Jan 29 2016|
|Event||15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States|
Duration: Nov 14 2015 → Nov 17 2015
|Name||Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Other||15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Period||11/14/15 → 11/17/15|
Bibliographical noteFunding Information:
This research was supported in part by the National Science Foundation Awards 1029711, 0905581, and 1464297. The NASA Award NNX12AP37G and an UMII MnDRIVE Fellowship. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
© 2015 IEEE.
- Burned area detection
- Time series
- change detection
- noise and outlier