• DocumentCode
    2808064
  • Title

    Experiment about Image Emporium Buildings Intelligent Recognition Using Spatial Semantic Model

  • Author

    Zhan Yunjun ; Yuan Yanbin ; Wu Yanyan ; Qi Peipei

  • Author_Institution
    Resources & Environ. Eng. Coll., Wuhan Univ. of Technol., Wuhan, China
  • fYear
    2009
  • fDate
    11-13 Dec. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Automatically extraction model, which is based on shape, spectrum, texture and other characteristics information, achieves a better effect in the information extraction of the buildings, but it is difficult to identify the building further. Therefore, this paper proposes we should consider the spatial association features and semantic features characteristic of the objectives when identifying the types of the buildings. This paper takes the emporium construction as the experiment objectives. In the base of analyzing the integrated features of the emporium construction, building emporium and construction spatial semantic model, automatically extracting the emporium construction information from the high-resolution remote sensing image and then testing and analyzing the recognition results. The result shows that this method is an effective way to automatically identify the building classification from the remote sensing image.
  • Keywords
    feature extraction; image classification; remote sensing; building classification; image emporium buildings; information extraction; intelligent recognition; remote sensing; spatial semantic model; Buildings; Data mining; Educational institutions; Gray-scale; Image analysis; Image recognition; Information analysis; Intelligent structures; Remote sensing; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4507-3
  • Electronic_ISBN
    978-1-4244-4507-3
  • Type

    conf

  • DOI
    10.1109/CISE.2009.5362834
  • Filename
    5362834