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Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

Abstract : The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.
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Lynda Khiali, Mamoudou Ndiath, Samuel Alleaume, Dino Ienco, Kenji Ose, et al.. Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach. International Journal of Applied Earth Observation and Geoinformation, Elsevier, 2019, 74, pp.103-119. ⟨10.1016/j.jag.2018.07.014⟩. ⟨hal-01931475⟩

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