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

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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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Authors and Affiliations

  1. Université Savoie Mont Blanc, Polytech Annecy-Chambéry, LISTIC, B.P. 80439, 74944, Annecy-le-Vieux, Annecy Cedex, France

    Nicolas Méger, Tuan Nguyen & Felicity Lodge

  2. Univ Lyon, INSA-Lyon, CNRS, INRIA, LIRIS, UMR5205, 69621, Villeurbanne, France

    Christophe Rigotti

  3. Univ Lyon, INSA-Lyon, CNRS, LIRIS, UMR5205, 69621, Villeurbanne, France

    Catherine Pothier

  4. Uber Technologies, 400 Centennial Parkway, Louisville, CO, 80027, USA

    Lionel Gueguen

  5. Bluecham SAS, 98807, Nouméa, Nouvelle-Calédonie, France

    Rémi Andreoli

  6. Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerre, 38000, Grenoble, France

    Marie-Pierre Doin

  7. German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234, Oberpfaffenhofen, Weßling, Germany

    Mihai Datcu

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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.

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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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