MURAL - Maynooth University Research Archive Library

    Segmentation Evaluation for Object Based Remotely Sensed Image Analysis

    Corcoran, Padraig and Winstanley, Adam C. (2007) Segmentation Evaluation for Object Based Remotely Sensed Image Analysis. In: Proceedings of the Geographical Information Science Research UK Conference, 11th - 13th April 2007, NUI Maynooth, Ireland .

    [img] Download (354kB)
    Official URL:

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    Object based image analysis (OBIA) is a relatively new form of remote sensing which aims to overcome the failings of traditional pixel based techniques at providing accurate land-use classification for high resolution data. The failure of pixel based techniques is due to the fact that these techniques are based on the assumption that individual classes contain uniform visual properties. As we increase the spatial resolution of data the intra-class variation increases and this property of class uniformity is broken leading to very poor performance (Blaschke 2003). The human visual system (HVS) can interpret high resolution data very easily and accurately. If a truly accurate and robust automated land-use classification system is to be achieved, it must draw from research in the area of cognitive psychology and attempt to model how we as humans interpret aerial imagery. This is the aim of research in the area of OBIA.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: OBIA; Segmentation; Texture; Evaluation.
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 1329
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 21 Apr 2009 15:40
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads