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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    Title :

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


    Contributors:
    Li, Yibo (author) / Zhang, Senyue (author) / Zhao, Jingfei (author) / Tan, Wenan (author)


    Publication date :

    2017-12-01


    Size :

    196191 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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