DocumentCode :
3739341
Title :
Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping
Author :
Xi C. Chen;Vipin Kumar;James H Faghmous
Author_Institution :
Dept. of Comput. Sci., Univ. of Minnesota, Minneapolis, MN, USA
fYear :
2015
Firstpage :
1536
Lastpage :
1537
Abstract :
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.
Keywords :
"Time series analysis","Remote sensing","Earth","Monitoring","Detection algorithms","Noise measurement","Gaussian processes"
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2015 IEEE International Conference on
Electronic_ISBN :
2375-9259
Type :
conf
DOI :
10.1109/ICDMW.2015.237
Filename :
7395855
Link To Document :
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