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Spatial-feature data cube for spatiotemporal remote sensing data processing and analysis

  • Dong Xu
  • Yan MaEmail author
  • Jining YanEmail author
  • Peng Liu
  • Lajiao Chen
Article
  • 36 Downloads

Abstract

With the rapid development in Earth observation technology, a variety of satellite sensors have provided large and open sets of remote sensing data. However, traditional methods of analysis are no longer available for time-serial remote sensing data analysis that typically handles multidimensional spatio-temporal data models. Moreover, researchers have found it trivial and tedious to obtain ready-to-analyze data for Earth science models from regular Earth observation data. For an easy and efficient time-serial remote sensing data analysis, a spatial-featured data cube analysis tool based on multidimensional data model is proposed for time-serial remote sensing data processing and analysis. For the performance consideration, a distributed execution engine was also used for efficient implementation of large-scale tasks in parallel. Finally, through experiments on both normalized difference vegetation index product and water detection within a 20-year period, we confirmed that our approach is efficient and scalable for a long time-series analysis.

Keywords

Spatial feature data cube Long time series analysis Multi-dimensional data Remote sensing data processing 

Mathematics Subject Classification

86 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 41401512), National Natural Science Foundation of China (No. 41471368), National Key Research and Development Plan of China (No. 2016YFA0600302), and Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. Y6YR0300QM).

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Digital EarthUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.China University of GeosciencesWuhanPeople’s Republic of China

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