Satellite image time‐series (SITS) are multidimensional signals of high complexity. Their main characteristics are spatio‐temporal patterns which describes the scene dynamics. The information contained in SITS was coded using Bayesian methods, resulting in a graph representation.
This paper further presents a concept of interactive learning for semantic labeling of spatio‐temporal patterns present in SITS. It enables the recognition and the probabilistic retrieval of similar events. Graphs are attached to statistical models for spatio‐temporal processes, which at their turn describe physical changes in the observed scene. Therefore, user‐specific semantics attached to spatio‐temporal events are modeled using combinations of parameters of a distance model between sub‐graphs. Thus, the learning step is performed by the incremental definition of a spatio‐temporal event type via user‐provided positive and negative sub‐graph examples. From these examples we infer probabilities of the Bayesian network, based on a Dirichlet model, that links user interest to a specific similarity measurement. According to the current state of learning, sub‐graph posterior probabilities are estimated. Experiments, performed on a multitemporal SPOT image time‐series, demonstrate the presented reasoning concept.
