Defining decision region borders properly is a major task of classification algorithms. In this paper, the border feature detection and adaptation (BFDA) algorithm is introduced for this purpose. The BFDA is a novel classification scheme, especially useful for the classification of remote sensing images. The method exploits the powerful discrimination capability of the 1-Nearest Neighborhood (1-NN) method with the border feature vectors. The first part of the algorithm consists of generating border feature vectors using class centers and misclassified training vectors. With this approach, a manageable number of border feature vectors are obtained. The second part of the algorithm involves the adaptation of the border feature vectors with a technique similar to the learning vector quantization (LVQ) algorithm. The performance of the BFDA was compared with other classification algorithms including support vector machines (SVMs) and several statistical classification techniques.


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    Title :

    Border Feature Detection and Adaptation Algorithm and Its Application in Remote Sensing


    Contributors:


    Publication date :

    2007-06-01


    Size :

    4142502 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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