Evaluation of Neural Pattern Classifiers for a Remote Sensing Application

    Publikation: Working/Discussion PaperWU Working Paper

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    This paper evaluates the classification accuracy of three neural network classifiers on a satellite
    image-based pattern classification problem. The neural network classifiers used include two types
    of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal
    (conventional) classifier is used as a benchmark to evaluate the performance of neural network
    classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a
    Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to
    evaluation of classification accuracy, the neural classifiers are analysed for generalization capability
    and stability of results. Best overall results (in terms of accuracy and convergence time) are
    provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and
    requires no problem-specific system of initial weight values. Its in-sample classification error is
    7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of
    simulations serve to illustrate the properties of the classifier in general and the stability of the result
    with respect to control parameters, and on the training time, the gradient descent control term,
    initial parameter conditions, and different training and testing sets. (authors' abstract)
    HerausgeberWU Vienna University of Economics and Business
    PublikationsstatusVeröffentlicht - 1 Mai 1995


    ReiheDiscussion Papers of the Institute for Economic Geography and GIScience

    WU Working Paper Reihe

    • Discussion Papers of the Institute for Economic Geography and GIScience