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Exploring high repetitivity remote sensing time series for mapping and monitoring natural habitats — A new approach combining OBIA and k-partite graphs

Abstract : High repetitivity remote sensing could substantially improve natural habitats monitoring and mapping in the next years. However, dense time series of satellite images require new processing methodologies. In this paper we proposed an approach which combines Object Based Image Analysis (OBIA) and k-partite graphs for detecting spatiotemporal evolutions in a Mediterranean protected site composed of several types of natural and semi-natural habitats. The method was applied over a recent dataset (SPOT4 Take-5) specially conceived to simulate the acquisition frequency of the future Sentinel-2 satellites. The results indicate our method is capable to synthesize complex spatiotemporal evolutions in a semi-automatic way, therefore offering a new tool to analyze high repetitivity satellite time series.
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Fabio Güttler, Samuel Alleaume, Christina Corbane, Dino Ienco, J. Nin, et al.. Exploring high repetitivity remote sensing time series for mapping and monitoring natural habitats — A new approach combining OBIA and k-partite graphs. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2014, Quebec City, Canada. pp.3930-3933, ⟨10.1109/IGARSS.2014.6947344⟩. ⟨hal-02108532⟩

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