Maritime ship tracking is a crucial component of maritime surveillance, holding paramount importance in both military and civilian spheres. This study proposes a maritime ship tracking concept utilizing Synthetic Aperture Radar (SAR) constellations, along with a Detection-Matching-Tracking (DMT) implementation strategy. Specifically, we design a novel SAR ship detector capable of locating and segmenting all ships present within image sequences provided by an SAR constellation. Following ship detection, we employ an enhanced two-channel convolutional neural network (2-channel CNN) to perform ship matching between the target ship and potential candidates. Ultimately, based on the matching results, we can plot the space-time trajectory of the tracked ship. The preliminary experiment demonstrates that the proposed methodology is feasible and has the potential to track ships in open seas.


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    Titel :

    Maritime ship tracking based on SAR constellation: a preliminary implementation


    Beteiligte:
    Bruzzone, Lorenzo (Herausgeber:in) / Bovolo, Francesca (Herausgeber:in) / Lang, Haitao (Autor:in) / Niu, Lihui (Autor:in) / Li, Jialu (Autor:in) / Li, Wenjing (Autor:in) / Ma, Li (Autor:in)

    Kongress:

    Image and Signal Processing for Remote Sensing XXIX ; 2023 ; Amsterdam, Netherlands


    Erschienen in:

    Proc. SPIE ; 12733


    Erscheinungsdatum :

    19.10.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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