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      SAR ATR Based on FCNN and ICAE

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          Abstract

          In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 October 2018
          : 7
          : 5
          : 622-631
          Affiliations
          [1 ] ①(School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
          [2 ] ③(School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China)
          [3 ] ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
          Article
          bb70af9c8098457c95e451869303cff8
          10.12000/JR18066

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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