Evaluating neural networks and evidence pooling for land cover mapping

M. J. Aitkenhead, S. Flaherty, M. E. J. Cutler

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    The diversity of data sources, analysis methodologies, and classification systems has led to a number of new techniques for monitoring land-cover change. However, this wide choice means that it is difficult to know which solution to choose. A system capable of integrating the results of different analyses and applying them to land-cover mapping would therefore be extremely useful. This study investigates the use of evidence pooling and neural networks in land-cover mapping. Neural networks were used to classify land-cover using evidence from spectral (Landsat-7 ETM1), textural, and topographic information. Mapping was performed using combinations of evidence source and evidence pooling techniques. The best performance was achieved using all available information with a method that summed evidence directly instead of categorizing it. While the methodology failed to reach the level of accuracy recommended elsewhere, a comparison of the number of classes used with other methods showed that the system performed better than these approaches. © 2008 American Society for Photogrammetry and Remote Sensing.
    Original languageEnglish
    Pages (from-to)1019-1032
    Number of pages14
    JournalPhotogrammetric Engineering and Remote Sensing
    Issue number8
    Publication statusPublished - Aug 2008


    • Artificial intelligence
    • Computer networks
    • Conformal mapping
    • Accuracy assessment
    • Neural networks
    • Classification
    • Land cover
    • Landsat
    • Topographic mapping


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