GAO Fei, WANG Meng, WANG Jun, YANG Erfu, ZHOU Huiyu. A Novel Separability Objective Function in CNN for Feature Extraction of SAR Images[J]. Chinese Journal of Electronics, 2019, 28(2): 423-429. doi: 10.1049/cje.2018.12.001
Citation: GAO Fei, WANG Meng, WANG Jun, YANG Erfu, ZHOU Huiyu. A Novel Separability Objective Function in CNN for Feature Extraction of SAR Images[J]. Chinese Journal of Electronics, 2019, 28(2): 423-429. doi: 10.1049/cje.2018.12.001

A Novel Separability Objective Function in CNN for Feature Extraction of SAR Images

doi: 10.1049/cje.2018.12.001
Funds:  This research was funded by the National Natural Science Foundation of China (No.61771027, No.61071139, No.61471019, No.61501011, No.61171122). E. Yang is supported in part under the RSE-NNSFC Joint Project (2017-2019) (No.6161101383) with China University of Petroleum (Huadong). H. Zhou is supported by Invest NI/Philips, UK EPSRC (No.EP/N011074/1) and Royal Society-Newton Advanced Fellowship (No.NA160342).
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  • Corresponding author: WANG Jun (corresponding author) was born in 1972. He received the B.S. degree from the Northwestern Polytechnical University, Xi'an, China, in 1995 and the M.S. and Ph.D. degrees from Beihang University (BUAA), Beijing, China, in 1998 and 2001, respectively. He is currently a professor with the School of Electronic and Information Engineering, BUAA. His research interests include signal processing, DSP/FPGA real time architecture, target recognition and tracking. (Email:wangj203@buaa.edu.cn)
  • Received Date: 2018-04-12
  • Rev Recd Date: 2018-08-11
  • Publish Date: 2019-03-10
  • Convolutional neural network (CNN) has become a promising method for Synthetic aperture radar (SAR) target recognition. Existing CNN models aim at seeking the best separation between classes, but rarely care about the separability of them. We performs a separability measure by analyzing the property of linear separability, and proposes an objective function for CNN to extract linearly separable features. The experimental results indicate the output features are linearly separable, and the classification results are comparable with the other state of the art techniques.
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