11 April 2018 Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition
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Abstract
A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.
© 2018 SPIE and IS&T
Yue Yan, "Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition," Journal of Electronic Imaging 27(2), 023024 (11 April 2018). https://doi.org/10.1117/1.JEI.27.2.023024 Submission: Received 10 January 2018; Accepted 26 March 2018
Submission: Received 10 January 2018; Accepted 26 March 2018
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