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Mathematical Models for Remote Sensing Image Processing pp 357–398Cite as

Satellite Image Time Series: Mathematical Models for Data Mining and Missing Data Restoration

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Part of the book series: Signals and Communication Technology ((SCT))

Abstract

One of the exceptional advantages of spaceborne remote sensors is their regular scanning of the Earth surface, resulting thus in Satellite Image Time Series (SITS), extremely useful to monitor natural or man-made phenomena on the ground. In this chapter, after providing a brief overview of the most recent methods proposed to process and/or analyze time series of remotely sensed data, we describe methods handling two issues: the unsupervised exploration of SITS and the reconstruction of multispectral images. In particular, we first present data mining methods for extracting spatiotemporal patterns in an unsupervised way and illustrate this approach on time series of displacement measurements derived from multitemporal InSAR images. Then we present two methods which aim to reconstruct multispectral images contaminated by the presence of clouds. The first one is based on a linear contextual prediction mode that reproduces the local spectro-temporal relationships characterizing a given time series of images. The second method tackles the image reconstruction problem within a compressive sensing formulation and with different implementation strategies. A rich set of illustrations on real and simulated examples is provided and discussed.

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Notes

  1. 1.

    The authors wish to thank the ANR EFIDIR and ANR FOSTER projects for funding the works presented in this chapter.

  2. 2.

    We thank Romain Jolivet (California Institute of Technology-CALTECH, Seismological laboratory) and Cécile Lasserre (Centre National de la Recherche Scientifique-CNRS, ISTerre laboratory) for computing and providing this InSAR time series.

  3. 3.

    We thank the European Space Agency (ESA) for providing this ENVISAT SAR series (Dragon project ID5305).

  4. 4.

    We thank Romain Jolivet (California Institute of Technology-CALTECH, Seismological laboratory), Cécile Lasserre (Centre National de la Recherche Scientifique-CNRS, ISTerre laboratory), and Catherine Pothier (Institut National des Sciences Appliquées de Lyon-INSA Lyon, LGCIE laboratory) for this interpretation.

  5. 5.

    We thank Romain Jolivet (California Institute of Technology-CALTECH, Seismological laboratory), Cécile Lasserre (Centre National de la Recherche Scientifique-CNRS, ISTerre laboratory) and Catherine Pothier (Institut National des Sciences Appliquées de Lyon-INSA Lyon, LGCIE laboratory) for this interpretation.

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Authors and Affiliations

  1. LISTIC laboratory, Polytech Annecy-Chambéry, Université Savoie Mont Blanc, BP 80439, 74944, Annecy cedex, France

    Nicolas Méger & Emmanuel Trouvé

  2. Centre for Integrative Biology, University of Trento, Via Sommarive 9, 38123, Trento, Italy

    Edoardo Pasolli

  3. INSA-Lyon, LIRIS, UMR5205, University Lyon, CNRS, INRIA, 20 av. A. Einstein, F-69621, Villeurbanne, France

    Christophe Rigotti

  4. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Trento, Italy

    Farid Melgani

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  1. Nicolas Méger
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  2. Edoardo Pasolli
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  3. Christophe Rigotti
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  4. Emmanuel Trouvé
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  5. Farid Melgani
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Correspondence to Farid Melgani .

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  1. University of Genoa, Genoa, Italy

    Gabriele Moser

  2. Université Côte d’Azur, Inria, Sophia Antipolis Cedex, France

    Josiane Zerubia

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Méger, N., Pasolli, E., Rigotti, C., Trouvé, E., Melgani, F. (2018). Satellite Image Time Series: Mathematical Models for Data Mining and Missing Data Restoration. In: Moser, G., Zerubia, J. (eds) Mathematical Models for Remote Sensing Image Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-66330-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-66330-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66328-9

  • Online ISBN: 978-3-319-66330-2

  • eBook Packages: EngineeringEngineering (R0)

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