Image segmentation - Its application to information extraction for updating geo-spatial databases

Juan Gu*, Jun Chen, Qiming ZHOU

*Corresponding author for this work

    Research output: Contribution to journalConference articlepeer-review

    1 Citation (Scopus)


    The unsatisfactory result of traditional pixel-based classification methods in classifying high resolution remotely sensed imagery may be improved by employing image segmentation. Based on a brief review of image segmentation, this paper introduces an image segmentation method - FNEA - which is used in eCognition, the first commercial object-oriented image processing software in the world, for automatic object extraction from high-resolution satellite images and automatic updating of GIS databases. From the point of information extraction, the author analyzes the advantages and disadvantages of the algorithm by using several examples and put forward possible improvements.

    Original languageEnglish
    Article number60441B
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Publication statusPublished - 2005
    EventMIPPR 2005: Image Analysis Techniques - Wuhan, China
    Duration: 31 Oct 20052 Nov 2005

    Scopus Subject Areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

    User-Defined Keywords

    • FNEA
    • High resolution remotely sensed imagery
    • Image segmentation
    • Information extraction


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