TRAJECTORY OF DYNAMIC CLUSTERS IN IMAGE TIME-SERIES
Patrick Héas, IRIT, DLR, Mihai P. Datcu, Alain Giros · 2004
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. Index Terms — Spatio-temporal learning, information mining, Bayesian modeling, dynamic cluster trajectories, semantic labeling.