SAR ATR Based on FCNN and ICAE

Leida xuebao

Wang Yadong¹  Yu Lingjuan¹  Lin Yun²  Hong Wen²  Xie Xiaochun³ 

[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(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China);③(School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China);

关键词: Synthetic Aperture Radar (SAR);    Automatic target recognition;    Fully Convolutional Neural Network (FCNN);    Convolutional Auto-Encoder (CAE);    Improved Convolutional Auto-Encoder (ICAE);   

DOI  :  10.12000/JR18066

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown