SAR satellites, unaffected by illumination and weather conditions, enable round-the-clock and all-weather earth observation, offering numerous distinct advantages in monitoring ships and their tracks on the sea surface. Therefore, this paper selects images from three C-band radar satellites, namely GF-3, GF-3B, and GF-3C, to create a remote sensing dataset for ship wake recognition. The YOLOv8-OBB model is trained and utilized for the identification of ship wakes. Experimental results demonstrate that, compared to the YOLOv8 model, YOLOv8-OBB exhibits superior recognition performance, making it more suitable for obtaining motion information such as ship positions and headings. The recognition accuracy reaches 86%. This method can be applied to tasks such as maritime ship monitoring, holding significant importance for assessing maritime economic and trade activities, ensuring energy transportation, and safeguarding sea surface security.


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    Title :

    Ship wake target detection in C-SAR image based on YOLOv8-OBB


    Contributors:
    Debayle, Johan (editor) / Patel, Nilanchal (editor) / Qi, Wenping (author) / Zhao, Xinwei (author) / Zeng, Yong (author) / Huang, Shusong (author) / Yuan, Zheng (author)

    Conference:

    International Conference on Remote Sensing Technology and Image Processing (RSTIP 2024) ; 2024 ; Dali, China


    Published in:

    Proc. SPIE ; 13640


    Publication date :

    2025-06-03





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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