A combination of a convolutional neural network, which belongs to the deep learning research field, and support vector machines is presented as an efficient automatic target recognition system. Additional training methods that incorporate prior knowledge to the classifier and further improve its robustness against imaging errors and target variations are also presented. These methods generate artificial training data by elastic distortion and affine transformations that represent typical examples of image errors, like a changing range scale dependent on the depression angle or an incorrectly estimated aspect angle. With these examples presented to the classifier during the training, the system should become invariant against these variations and thus more robust. For the classification, the spotlight synthetic aperture radar images of the moving and stationary target acquisition and recognition database are used. Results are shown for the ten class database with a forced decision classification as well as with rejection class.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    SAR ATR by a combination of convolutional neural network and support vector machines


    Contributors:


    Publication date :

    2016-12-01


    Size :

    2117070 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Development of Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines

    Nagata, Fusaomi / Tokuno, Kenta / Otsuka, Akimasa et al. | Springer Verlag | 2019


    Nonlinear Combination Forecasting Method Based on Wavelet Support Vector Machines

    Li, Y.-c. / Li, B. / Fang, T.-j. | British Library Online Contents | 2004


    Support vector machines

    Garay, Michael J. / Mazzoni, Dominic / Davies, Roger et al. | NTRS | 2004


    Training invariant support vector machines

    Decoste, D. / Scholkopf, B. | NTRS | 2002