Identifying dynamic changes with noisy labels in spatial-temporal data: A study on large-scale water monitoring application

Xiaowei Jia, Xi Chen, Anuj Karpatne, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The need for effective change detection is ever growing with more emerging large-scale spatial-temporal datasets that contain gridded time series data. To detect meaningful changing events with respect to our desired characteristics, in this paper we focus on the post-classification change detection problem which aims to apply change detection techniques on the time series of classification outputs. To study the challenges and to evaluate the performance, we apply the change detection techniques to an application of water monitoring using remote sensing data. Since the learning model can be affected by special properties of remote sensing data, the obtained classification outputs usually contain much noise. Therefore the successful change detection requires an elaborate mechanism to handle the time series of noisy classification outputs. To this end we propose to integrate spatial and temporal constraints into an optimization based change detection framework. The proposed framework mitigates the noise in the time series and can be efficiently solved by an EM-style algorithm. The extensive experimental results on both synthetic and real-world datasets very well demonstrate the effectiveness of the proposed method in detecting the water dynamics.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1328-1333
Number of pages6
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • Change Detection
  • Spatiotemporal Data
  • Water Monitoring

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