Semantic-based user demand modeling for remote sensing images retrieval

This paper aims at providing a more convenient approach for remote sensing images retrieval based on sematic-based user demand modeling. The semantic user demand model is a two-layer model that bridges the gap between users´ satellite image demand of natural language description and satellite images. Natural language process and semantic inference are involved to generate the semantic user demand model. A knowledge database consists of ontologies, rules and dictionaries is developed to support natural language process and semantic inference. Semantic similarity and confliction-resolution are also adopted in inference. Finally, the model is validated by a prototype system based on protégé-owl and JESS. The results show that the model and the approach are available.