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11 September 2019 Deep convolutional neural network structural design for synthetic aperture radar image target recognition based on incomplete training data and displacement insensitivity
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Abstract

Deep convolutional neural networks (DCNN) are extensively used in image classification. However, it is typically impossible to receive ideal effects if directly using it in synthetic aperture radar (SAR) image target recognition. During the data acquisition process, it is difficult to acquire a large number of labeled training data and to ensure the image targets in the center. To extract the width and height features of SAR images and reduce the computational complexity, we constructed an asymmetric parallel convolution module. The module avoids severe over-fitting due to limited training samples and effectively deals with displacement changes with test samples. Meanwhile, the residual learning method is used in the algorithm to avoid the deep network degradation and improve algorithm recognition accuracy (RA). Experimental results show that the RA of the DCNN with residual-learning-based APCRLNet reaches 99.75% under standard operating conditions, which is superior to existing recognition methods. Furthermore, the algorithm also performs well for incomplete training samples and test samples with displacement changes.

© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Yuchao Hou, Yanping Bai, Ting Xu, Huichao Yan, Yan Hao, Dunwei Du, and Yanjie Guo "Deep convolutional neural network structural design for synthetic aperture radar image target recognition based on incomplete training data and displacement insensitivity," Journal of Electronic Imaging 28(5), 053002 (11 September 2019). https://doi.org/10.1117/1.JEI.28.5.053002
Received: 21 January 2019; Accepted: 11 June 2019; Published: 11 September 2019
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