We present a textural kernel for "support vector machines" classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We introduce kernel functions in order to favor the distinction between our class of interest and the other classes: it gives information of similarity. In our case this similarity is based on radiometric and textural characteristics. One of the main difficulties is to elaborate textural parameters which are relevant and characterize as well as possible the joint distribution of a set of connected pixels. We apply this method to remote sensing problems: the detection of forest fires and the extraction of urban areas in high resolution images.


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    Title :

    Textural kernel for SVM classification in remote sensing: application to forest fire detection and urban area extraction


    Contributors:
    Lafarge, F. (author) / Descombes, X. (author) / Zerubia, J. (author)


    Publication date :

    2005-01-01


    Size :

    266300 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Textural Kernal for SVM Classification in Remote Sensing: Application to Forest Fire Detection and Urban Area Extraction

    Lafarge, F. / Descombes, X. / Zerubia, J. | British Library Conference Proceedings | 2005



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