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Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning
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.
Cited literature [17 references]
https://hal.inria.fr/hal-00670342
Contributor : Patrick Héas <>
Submitted on : Wednesday, February 15, 2012 - 11:19:35 AM
Last modification on : Saturday, March 20, 2021 - 3:20:25 AM
Long-term archiving on: : Wednesday, May 16, 2012 - 2:21:08 AM
Contributor : Patrick Héas <>
Submitted on : Wednesday, February 15, 2012 - 11:19:35 AM
Last modification on : Saturday, March 20, 2021 - 3:20:25 AM
Long-term archiving on: : Wednesday, May 16, 2012 - 2:21:08 AM
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IEEEGRS_HeasDatcu.pdf
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