Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment

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• 作者：Lianlian He (1) (2)
  Peng Yue (3) (4)
  Liangcun Jiang (3)
  Mingda Zhang (3)
  
  1. School of Mathematics and Statistics ; Wuhan University ; Wuhan ; China
  2. Department of Mathematics ; Hubei University of Education ; Hubei ; China
  3. State Key Laboratory of Information Engineering in Surveying ; Mapping and Remote Sensing ; Wuhan University ; Wuhan ; China
  4. Key Laboratory of Poyang Lake Wetland and Watershed Research ; Ministry of Education ; Jiangxi Normal University ; Nanchang ; 330022 ; China
  
• 关键词：Fuzzy logic ; Fuzzy spatial relation ontology ; Geoprocessing workflow ; Remote sensing imagery ; Complex geospatial feature ; Geospatial service
• 刊名：Earth Science Informatics
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：8
• 期：1
• 页码：63-76
• 全文大小：2,947 KB
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• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Computer Applications in Geosciences
  Geosciences
  Simulation and Modeling
  
• 出版者：Springer Berlin Heidelberg
• ISSN：1865-0481

文摘

Remote sensing images have been widely used by intelligence analysts to discover geospatial features. The overwhelming volume of remote sensing imagery requires automated methods or systems for feature discovery. Existing research focuses on automatic extraction of isolated or elementary features, such as buildings and roads. It is rather understudied to discover complex geospatial features, which is spatially composed of elementary features. From the e-Science perspective, service computing technologies have shown great promise for widespread automation of data analysis and computation. The discovery of complex features would benefit from service computing technologies by computing spatial relations and their fuzziness among elementary features using geoprocessing services. The discovery process can be automated using an ontology approach. The paper presents how ontologies for complex geospatial features, enriched with fuzzy sets of spatial relations, can automate the workflow generation. Spatial computation functions, fuzzy membership functions, and mathematical fuzzy logical operators, are provided as services, and plugged into workflows on demand to enjoy the benefits of service computing technologies. A prototype system demonstrates on-demand uncertainty-aware detection of complex geospatial features in a geoprocessing service environment.