In this study hierarchical classification structure and the feature extraction method based on texture features are applied to SAR data. The most important feature of hierarchical classification is to break down a complex decision-making process into a collection of simpler decisions. In order to achieve more complex analysis it is advantageous to use binary decision trees, in which the decision between only two classes must be assigned at each node. Pixel based feature extraction methods reduce classification performance because of the speckle and also conventional texture analysis is not applicable to every part of an image. Therefore, a decision-making process, which can be applied to every pixel of an image, is required. The results show that computation time and accuracy of classification process are improved.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hierarchical classification of SAR data with feature extraction method based on texture features


    Beteiligte:
    Kasapoglu, N.G. (Autor:in) / Yazgan, B. (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    307549 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Hierarchial Classification of SAR Data with Feature Extraction Method Based on Texture Features

    Kasapoglu, G. / Yazgan, B. | British Library Conference Proceedings | 2003



    Neural Network-Based Color Texture Classification with Wavelet Packets for Feature Extraction

    Rani, B. S. / Renganathan, S. | British Library Online Contents | 2004



    Texture Classification Using Combined Feature Sets

    Ng, L. S. / Nixon, M. S. / Carter, J. et al. | British Library Conference Proceedings | 1998