A maritime automatic target recognition system is developed that performs ship classification using one-dimensional high resolution range profiles. Five physically based features are defined and are extracted from both VV and HH polarizations resulting in a 10-dimensional feature vector. A nonlinear classifier combination approach involving a neural network combiner along with three individual classifiers (Bayes, nearest neighbor, and neural network) is proposed. A decision confidence measure based on the classifier discriminants is developed using a nonparametric estimation approach. The confidence measure enables the system to reject samples that have a low decision confidence. The performance of the proposed neural network based combination is compared with individual classifiers and a number of other combination rules. The results show that this combination can provide high recognition accuracy along with a high probability of declaration. The performance in the presence of samples from not-before-seen classes is also investigated. A new nearest neighbor confidence thresholding approach is developed to aid the neural network combiner in rejecting these samples.


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    Titel :

    Maritime ATR using Classifier Combination and High Resolution Range Profiles


    Beteiligte:
    Pilcher, C. M. (Autor:in) / Khotanzad, A. (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    4382650 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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