Aircraft detection in remote sensing images is always the research hotspot but a challenging task for the variations of aircraft type, pose, size and complex background. The paper proposes a region-based convolutional neural network to detect aircrafts. To enhance the learning ability of the network, a multi-resolution aircraft remote sensing dataset is collected from Google Earth. Then, the detection model is trained end to end by fine-tuning on the obtained dataset and realizes automatic aircraft recognition and positioning. Experiments show that the proposed method outperforms state-of-the-art method on the same dataset and the requirement for real-time can be satisfied simultaneously.


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    Titel :

    Aircraft Detection in Remote Sensing Images Based on Deep Convolutional Neural Network


    Beteiligte:
    Li, Yibo (Autor:in) / Zhang, Senyue (Autor:in) / Zhao, Jingfei (Autor:in) / Tan, Wenan (Autor:in)


    Erscheinungsdatum :

    01.12.2017


    Format / Umfang :

    196191 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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