Data Mining and Knowledge Discovery

, Volume 33, Issue 1, pp 131–167 | Cite as

Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

  • Nicolas MégerEmail author
  • Christophe Rigotti
  • Catherine Pothier
  • Tuan Nguyen
  • Felicity Lodge
  • Lionel Gueguen
  • Rémi Andreoli
  • Marie-Pierre Doin
  • Mihai Datcu
Part of the following topical collections:
  1. Special Issue on Data Mining for Geosciences


Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.


Satellite Image Time Series Summarization Swap randomization Mutual information Crustal deformation Environmental monitoring 



The Lansat 7 SITS was retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota The authors wish to thank the European Space Agency (ESA) for providing the ENVISAT SAR data over Mount Etna, and the Yaté rural district of New Caledonia for its support.


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© The Author(s) 2018

Authors and Affiliations

  1. 1.Université Savoie Mont Blanc, Polytech Annecy-Chambéry, LISTICAnnecy-le-Vieux, Annecy CedexFrance
  2. 2.Univ Lyon, INSA-Lyon, CNRS, INRIA, LIRIS, UMR5205VilleurbanneFrance
  3. 3.Univ Lyon, INSA-Lyon, CNRS, LIRIS, UMR5205VilleurbanneFrance
  4. 4.Uber TechnologiesLouisvilleUSA
  5. 5.Bluecham SASNouméaFrance
  6. 6.Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerreGrenobleFrance
  7. 7.German Aerospace Center (DLR)Remote Sensing Technology InstituteOberpfaffenhofen, WeßlingGermany

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