MURAL - Maynooth University Research Archive Library



    Data Fusion for Topographic Object Classification


    Keyes, L. and Winstanley, Adam C. (2001) Data Fusion for Topographic Object Classification. Proceedings IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas. pp. 275-279.

    [img] Download (78kB)


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    This paper presents research conducted into the automatic recognition of features and objects on topographic maps (for example, buildings, roads, land parcels etc.) using a selection of shape description methods developed mostly in the field of computer vision. In particular the work here focuses on the proposal and evaluation of fusion techniques (at the decision level of representation) for the classification of topographic data. A set of Ordnance Survey large-scale digital data (1:1250 and 1:2500) was used to evaluate the classification performance of the shape recognition methods used. Each technique proved partially successful in distinguishing classes of objects, however, no one technique provided a general solution to the problem. Further outlined experiments combine these techniques, using a data fusion methodology, on the real-world problem of checking and assigning feature codes in large-scale Ordnance Survey digital data.

    Item Type: Article
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 67
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 17 Dec 2002
    Journal or Publication Title: Proceedings IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas
    Publisher: IEEE
    Refereed: Yes
    URI:
      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

      Downloads per month over past year

      Origin of downloads