Study of deep transfer learning for SAR ATR based on simulated SAR images

doi:10.7523/j.issn.2095-6134.2020.04.011

Abstract

Abstract: Using deep convolutional neural networks to realize automatic target recognition of SAR requires a large amount of labeled data. In order to solve the problem caused by the scarcity of SAR real images, we propose a method for improving the target recognition performance of SAR by using simulated SAR images on convolutional neural networks improved by CReLU activation function and batch normalization. The method transfers the effective knowledge learned from a large number of simulated SAR images onto the real SAR images. In the training, the pre-trained convolutional neural networks can be obtained by training by using the simulated SAR images firstly, and the deep transfer learning method is used to effectively solve the problem caused by the insufficiency of SAR image data. The validation experiment is carried out on the MSTAR dataset. The highest recognition accuracy reaches 99.78%, and good recognition results are obtained based on a small amount of SAR image data.

Key words: SAR, simulated SAR images, transfer learning, ATR (automatic target recognition)

CLC Number:

TN957

WANG Zelong, XU Xianghui, ZHANG Lei. Study of deep transfer learning for SAR ATR based on simulated SAR images[J]. , 2020, 37(4): 516-524.

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