A graph-based approach to detect spatiotemporal dynamics in satellite image time series

Fabio Guttler, Dino Ienco, Jordi Nin, Maguelonne Teisseire, Pascal Poncelet

Research output: Indexed journal article Articlepeer-review

40 Citations (Scopus)

Abstract

Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

Original languageEnglish
Pages (from-to)92-107
Number of pages16
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume130
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

Keywords

  • Data mining
  • Graph-based techniques
  • Land-cover
  • Monitoring
  • OBIA
  • Satellite image time series

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