This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+




    Publication date :

    2024-09-04


    Size :

    2666059 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Automatic ship detection in SAR satellite images: Performance assessment

    Stasolla, Mattia / Santamaria, Carlos / Mallorqui, Jordi J. et al. | IEEE | 2015


    Unmanned Driving System Based on DeepLabV3+ Semantic Segmentation

    Wang, Hongyu / Ma, Jiefei / Chi, Haifei | IEEE | 2021


    Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware

    Lenz, Gregor / McLelland, Douglas | ArXiv | 2024

    Free access

    Ship Detection in Optical Satellite Images Based on Sparse Representation

    Zhou, Haotian / Zhuang, Yin / Chen, Liang et al. | Springer Verlag | 2017