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Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning

Patrick Héas 1 Mihai Datcu 2
1 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
CEMAGREF - Centre national du machinisme agricole, du génie rural, des eaux et forêts, Inria Rennes – Bretagne Atlantique
Abstract : During the last decades, satellites have acquired incessantly high resolution images of many Earth observation sites. New products have arisen from this intensive acquisition process : high resolution Satellite Image Time-Series (SITS). They represent a large data volume with a rich information content and may open a broad range of new applications. This article presents an information mining concept which enables a user to learn and retrieve spatio-temporal structures in SITS. The concept is based on a hierarchical Bayesian modeling of SITS information content which enables us to link the interest of a user to specific spatio-temporal structures. The hierarchy is composed of two inference steps : an unsupervised modeling of dynamic clusters resulting in a graph of trajectories, and an interactive learning procedure based on graphs which leads to the semantic labeling of spatio-temporal structures. Experiments performed on a SPOT image time-series demonstrate the concept capabilities.
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Patrick Héas, Mihai Datcu. Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2005. ⟨hal-00670342⟩

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