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

Nicolas Méger; Christophe Rigotti; Catherine Pothier; Tuan Nguyen; Felicity Lodge; Lionel Gueguen; Rémi Andréoli; Marie-Pierre Doin; Mihai Datcu

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

Nicolas Méger ^{1, 2, 3} Christophe Rigotti ^{4, 5, 6} Catherine Pothier ⁷ Tuan Nguyen ^{1, 2, 3, 4} Felicity Lodge ^{1, 2, 3} Lionel Gueguen ⁸ Rémi Andréoli ⁹ Marie-Pierre Doin ^{10, 1, 11, 12, 13, 14} Mihai Datcu ¹⁵

1 USMB [Université de Savoie] [Université de Chambéry] - Université Savoie Mont Blanc

2 EPU [Ecole Polytechnique Universitaire de l'Université de Savoie] - Polytech Annecy-Chambéry

3 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

4 DM2L - Data Mining and Machine Learning

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

5 BEAGLE - Artificial Evolution and Computational Biology

LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558, Inria Grenoble - Rhône-Alpes, LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

6 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

7 imagine - Extraction de Caractéristiques et Identification

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

8 Uber Technologies

9 Bluecham SAS [Nouméa]

10 UGA - Université Grenoble Alpes

11 CNRS - Centre National de la Recherche Scientifique

12 IRD - Institut de Recherche pour le Développement

13 IFSTTAR - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux

14 ISTerre - Institut des Sciences de la Terre

15 DLR - German Aerospace Center

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.

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

Document type :

Journal articles

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01898015
Contributor : Nicolas Méger <>
Submitted on : Tuesday, October 23, 2018 - 5:16:28 PM
Last modification on : Friday, April 5, 2019 - 8:21:16 PM
Long-term archiving on : Thursday, January 24, 2019 - 5:26:13 PM

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