Abstract Ship detection in remote sensing imagery has been widely applied in military and citizen applications, such as fishery management, vessel surveillance or marine safety and security. With the development of optical satellite, optical satellite imagery ship detection has caused a lot of attention. In this paper, we propose an offshore ship detection method based on sparse representation. First we employ histogram of oriented gradient (HOG) as the feature descriptor, then the HOG feature are extracted from training dataset. After feature extraction, all of samples are used to adaptively train a dictionary. Next, we encode HOG feature description of patches from test image by the dictionary. Finally, the sparse code and support vector machine (SVM) classification are employed in ship target validation and false alarms elimination. Experiments have shown better detection performance and stronger robustness of our method compared with other methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ship Detection in Optical Satellite Images Based on Sparse Representation


    Beteiligte:
    Zhou, Haotian (Autor:in) / Zhuang, Yin (Autor:in) / Chen, Liang (Autor:in) / Shi, Hao (Autor:in)


    Erscheinungsdatum :

    19.12.2017


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Ship detection from optical satellite images based on visual search mechanism

    Yang, Feng / Xu, Qizhi / Gao, Feng et al. | IEEE | 2015


    Multi-layer Sparse Coding Based Ship Detection for Remote Sensing Images

    Li, Zimeng / Yang, Daiqin / Chen, Zhenzhong | IEEE | 2015


    Automatic ship detection in SAR satellite images: Performance assessment

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


    Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

    Sharma, Rohit / Dahatonde, Shubham / Acharjya, Srimanta Kumar et al. | IEEE | 2024