Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping

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 obs...

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Bibliographic Details
Published in2015 IEEE International Conference on Data Mining Workshop (ICDMW) pp. 1536 - 1537
Main Authors Chen, Xi C., Kumar, Vipin, Faghmous, James H.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.11.2015
Subjects
Algorithms
burned area detection
Change detection
Combustion
Detection algorithms
Earth
Ecology
Gaussian processes
Land cover
Monitoring
Natural resources
noise and outlier
Noise measurement
Online
Remote sensing
Time series
Time series analysis
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Summary: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.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:2375-9259
DOI:10.1109/ICDMW.2015.237