Expert Knowledge-Based Method for Satellite Image Time Series Analysis and Interpretation

For many remote-sensing applications, there is usually a gap between the automatic analysis techniques and the direct expert interpretation. This semantic gap is all the more critical as the amount and diversity of satellite data increase. In this context, an important challenge is the integration of expert knowledge in automatic satellite image time series (SITS) analysis to improve results' reliability and precision. In this paper, we propose an original expert knowledge-based SITS analysis technique for land-cover monitoring and region dynamics assessing. Particularly, we are interested in extracting region temporal evolution similar to a given scenario proposed by the user, which can be useful in many applications such as urbanization and forest regions' monitoring. As a first step, with the formalization and exploitation of the expert semantic information, we construct a multitemporal knowledge base describing the remote-sensing scene ontology. Then, the temporal evolution of each region in the SITS is modeled by means of graph theory. Finally, given a user scenario, the most similar region temporal evolution is recognized using the marginalized graph kernel (MGK) similarity measure.