SEMANTIC-BASED USER DEMAND MODELING FOR REMOTE SENSING IMAGES RETRIEVAL

被引：5

作者：

Zhu, Xinyan ^{[1
]}

Li, Ming ^{[1
]}

Guo, Wei ^{[1
]}

Zhang, Xia ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China

来源：

2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年

关键词：

Remote sensing; Image retrieval; Expert systems; Inference mechanisms; Natural language processing;

D O I：

10.1109/IGARSS.2012.6350719

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 protege-owl and JESS. The results show that the model and the approach are available.

引用

页码：2902 / 2905

页数：4

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