The ability to reliably detect ships within the ocean is one of the core capabilities of Maritime Domain Awareness. Constant improvements are pursued in both computational speed and reliable responses to events by exploring new ship detection methods. One such improvement is the availability of Sentinel-1 imagery. Owing to the fact that ships are considered locally bright objects, we propose using the H-dome transform to process the SAR image and improve ship detectability. The method was tested against two Sentinel-1 images in both HH and HV polarizations containing 82 ships. The method improved the false alarm rates when compared to the conventional cell-averaging Constant False Alarm Rate method at a minor reduction in detection accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ship detection in Sentinel-1 imagery using the H-dome transformation


    Beteiligte:
    Schwegmann, C. P. (Autor:in) / Kleynhans, W. (Autor:in) / Salmon, B. P. (Autor:in) / Mdakane, L. (Autor:in)


    Erscheinungsdatum :

    01.07.2015


    Format / Umfang :

    637596 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Learning on Sentinel-1 SAR Imagery for Ship Detection Using YOLO-V8 Model

    Javed, Malik Ahmad / Naseer, Ehtasham / Siddique, Muhammad Adnan | IEEE | 2024


    TANK DOME STRUCTURE AND SHIP

    MOCHIZUKI RYOHEI / KIMOTO SHINYA / YOKOZAWA HITOSHI et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Ship detection in Cyprus EEZ using Sentinel 1 data

    Themistocleous, Kyriacos / Prodomou, Maria / Mettas, Christodoulos et al. | SPIE | 2020


    TANK DOME STRUCTURE AND SHIP

    MOCHIZUKI RYOHEI / KIMOTO SHINYA / YOKOZAWA HITOSHI et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Automated Marine Debris Detection from Sentinel-2 Satellite Imagery

    R., Priyadarshini / Arya, Varun / S., Sowmya Kamath | IEEE | 2024