Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine

Ce Zheng; Xue Jiang; Xingzhao Liu

doi:10.1117/1.JRS.11.046007

25 October 2017 Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine

Ce Zheng, Xue Jiang, Xingzhao Liu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046007 (October 2017). https://doi.org/10.1117/1.JRS.11.046007

Abstract

Convolutional neural network (CNN), as a vital part of the deep learning research field, has shown powerful potential for automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the high complexity caused by the deep structure of CNN makes it difficult to generalize. An improved form of CNN with higher generalization capability and less probability of overfitting, which further improves the efficiency and robustness of the SAR ATR system, is proposed. The convolution layers of CNN are combined with a two-dimensional principal component analysis algorithm. Correspondingly, the kernel support vector machine is utilized as the classifier layer instead of the multilayer perceptron. The verification experiments are implemented using the moving and stationary target acquisition and recognition database, and the results validate the efficiency of the proposed method.

Citation Download Citation

Ce Zheng, Xue Jiang, and Xingzhao Liu "Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine," Journal of Applied Remote Sensing 11(4), 046007 (25 October 2017). https://doi.org/10.1117/1.JRS.11.046007

Received: 23 April 2017; Accepted: 26 September 2017; Published: 25 October 2017

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