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

Abstract

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.

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Acknowledgements

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 https://lpdaac.usgs.gov/data_access/data_pool. 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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Correspondence to Nicolas Méger.

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Funding for this Project was provided by a Grant from la Région Auvergne-Rhône-Alpes (Tuan Nguyen’s Grant). It was also supported by the PHOENIX ANR-15-CE23-0012 Grant of the French National Agency of Research, and benefited from a Centre National de la Recherche Scientifique (CNRS) “Défi Mastodons” funding. Catherine Pothier and Christophe Rigotti are members of Laboratoire d’Excellence Intelligence des Mondes Urbains (LabEx IMU, ANR-10-LABX-0088) that provided complementary support.

Responsible editor: Jorge, Rui and Larrazabal.

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Méger, N., Rigotti, C., Pothier, C. et al. Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring. Data Min Knowl Disc 33, 131–167 (2019). https://doi.org/10.1007/s10618-018-0591-9

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Keywords

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