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

Xi C. Chen; Vipin Kumar; James H. Faghmous

doi:10.1109/ICDMW.2015.237

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

Xi C. Chen
, Vipin Kumar
, James H. Faghmous

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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.

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.
Pages	1536-1537
Number of pages	2
ISBN (Electronic)	9781467384926
DOIs	https://doi.org/10.1109/ICDMW.2015.237
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

Publication series

Name	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/Territory	United States
City	Atlantic City
Period	11/14/15 → 11/17/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 12 Responsible Consumption and Production

Keywords

Burned area detection
Time series
change detection
noise and outlier

Publisher link

10.1109/ICDMW.2015.237

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Chen, X. C., Kumar, V., & Faghmous, J. H. (2016). Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping. In X. Wu, A. Tuzhilin, H. Xiong, J. G. Dy, C. Aggarwal, Z.-H. Zhou, & P. Cui (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 1536-1537). Article 7395855 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2015.237

Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping. / Chen, Xi C.; Kumar, Vipin; Faghmous, James H.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1536-1537 7395855 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, XC, Kumar, V & Faghmous, JH 2016, Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping. in X Wu, A Tuzhilin, H Xiong, JG Dy, C Aggarwal, Z-H Zhou & P Cui (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015., 7395855, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1536-1537, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDMW.2015.237

Chen XC, Kumar V, Faghmous JH. Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping. In Wu X, Tuzhilin A, Xiong H, Dy JG, Aggarwal C, Zhou ZH, Cui P, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1536-1537. 7395855. (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). doi: 10.1109/ICDMW.2015.237

Chen, Xi C. ; Kumar, Vipin ; Faghmous, James H. / Online Change Detection Algorithm for Noisy Time-Series : An Application Tonear-Real Time Burned Area Mapping. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. editor / Xindong Wu ; Alexander Tuzhilin ; Hui Xiong ; Jennifer G. Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1536-1537 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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

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 = "Burned area detection, Time series, change detection, noise and outlier",

author = "Chen, \{Xi C.\} and Vipin Kumar and Faghmous, \{James H.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

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N2 - 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.

AB - 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.

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KW - Time series

KW - change detection

KW - noise and outlier

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 14 November 2015 through 17 November 2015

ER -

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

Abstract

Publication series

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Bibliographical note

UN SDGs

Keywords

Publisher link

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