Neural networks applied to the classification of remotely sensed data

Nanno Mulder, Lieuwe Jan Spreeuwers

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    Abstract

    A neural network with topology 2-8-8 is evaluated against the standard of supervised non-parametric maximum likelihood classification. The purpose of the evaluation is to compare the performance in terms of training speed and quality of classification. Classification is done on multispectral data from the Thematic Mapper(TM3,TM4) in combination with a ground reference class map. This type of data is familiar to professionals in the field of remote sensing. This means that the position of clusters in feature space is well known and understood, and that the spatial pattern is equally well known. As a spin-off, the application of a neural net to a classical task of statistical pattern recognition helps to demystify neurai networks.
    Original languageEnglish
    Title of host publication1991 International Geoscience and Remote Sensing Symposium (IGARSS)
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages2211-2213
    Number of pages3
    ISBN (Print)0-87942-675-6
    DOIs
    Publication statusPublished - 1 Jun 1991
    EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 1991: Remote Sensing: Global Monitoring for Earth Management - Helsinki, Finland
    Duration: 3 Jun 19916 Jun 1991

    Publication series

    NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
    PublisherIEEE
    ISSN (Print)2153-6996
    ISSN (Electronic)2153-7003

    Conference

    ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 1991
    Abbreviated titleIGARSS
    CountryFinland
    CityHelsinki
    Period3/06/916/06/91

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    Keywords

    • IR-96223
    • METIS-113392

    Cite this

    Mulder, N., & Spreeuwers, L. J. (1991). Neural networks applied to the classification of remotely sensed data. In 1991 International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2211-2213). (International Geoscience and Remote Sensing Symposium (IGARSS)). Piscataway, NJ: IEEE. https://doi.org/10.1109/IGARSS.1991.575480