Abstract Ship detection is an important issue in many aspects, vessel traffic services, fishery management and rescue. Synthetic aperture radar (SAR) can produce real high resolution images with relatively small aperture in sea surfaces. A novel method employing extreme learning machine is proposed to detect ship in SAR. After the image preprocessing, some features including HOG features, geometrical features and texture features are selected as features for ship detection. The experimental results demonstrate that the proposed ship detection method based on extreme learning machine is more efficient than other learning-based methods.


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    Titel :

    Extreme Learning Machine Based Ship Detection Using Synthetic Aperture Radar


    Beteiligte:
    Jia, Shu-li (Autor:in) / Qu, Chong (Autor:in) / Lin, Wenjing (Autor:in) / Cai, Shuhao (Autor:in) / Ma, Liyong (Autor:in)


    Erscheinungsdatum :

    17.10.2018


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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    Yang, C.-S. / European Space Agency | British Library Conference Proceedings | 2007


    SAR (Synthetic Aperture Radar) Detection of Ships and Ship Wakes

    T. Wahl / K. Eldhuset / K. Aksnes | NTIS | 1986