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By David Jin; Helen Zhang; X J Zhao

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Read Online or Download Advanced research on intelligent systems and mechanical engineering : selected, peer reviewed papers from the 2012 2nd International Conference on Intelligent Materials and Mechanical Engineering (MEE2012), December 22-23, 2012, Yichang, China PDF

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Additional resources for Advanced research on intelligent systems and mechanical engineering : selected, peer reviewed papers from the 2012 2nd International Conference on Intelligent Materials and Mechanical Engineering (MEE2012), December 22-23, 2012, Yichang, China

Sample text

The remote sensor satellite sends the parameters to the earth in a digit or analog format using wireless communication. However, users still have difficulty retrieving remote sensing data. Traditional text-based methods, which are based on optical properties of these devices and materials, usually retrieve abundant useless data. Users spend a great deal of time sifting through undesirable query results before finding the desired data [1]. In order to solve the problem mentioned above, we designed a metadata ontology and implemented semantic reasoning for remote sensing data.

Therefore, we employed it as concepts vocabulary in consideration of applicability and authority. In addition to the FGDC's standard, other metadata standards similarly could be utilized such as IOS 19115 and Dublin Core to enhance descriptive ability of our ontology. The design of metadata ontology RDF (Resource Description Framework) is a formal language for representing information about resources on the Web. In order to describe classes and properties in RDF, we made use of RDFS (RDF Schema) to provide a vocabulary of domain specific classes and their relationships.

Through these characteristics it is available to identify the object types, surface temperature and a series of other characteristics. Thermal infrared remote sensing technology is widely used such as city surface temperature inversion, earthquake and volcano monitoring, forest fire and meteorological etc[2,3]. In this paper thermal infrared remote sensing technology is used to identify the surface material temperature abnormal area. 5m ), 30 m resolution remote sensing data of DEM, Google Earth remote sensing data.

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