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

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外文摘要：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.

外文关键词：

Synthetic aperture radar (SAR)Convolution neural network (CNN)ClassificationLinear separabilityObjective function

作者：

GAO Fei、WANG Meng、WANG Jun、YANG Erfu、ZHOU Huiyu

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作者单位：

Department of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Department of Design, Manufacture and Engineering Management (DMEM), University of Strathclyde, Glasgow G11XQ, UK

Department of Informatics, University of Leicester, Leicester LE17RH, UK

基金：

This research was funded by the National Natural Science Foundation of ChinaThis research was funded by the National Natural Science Foundation of ChinaThis research was funded by the National Natural Science Foundation of ChinaThis research was funded by the National Natural Science Foundation of ChinaThis research was funded by the National Natural Science Foundation of ChinaE.Yang is supported in part under the RSE-NNSFC Joint Project(2017-2019)H.Zhou is supported by Invest NI/Philips,UK EPSRCRoyal Society-Newton Advanced Fellowship

项目编号：

61771027No.61071139No.61471019No.61501011No.611711226161101383EP/N011074/1NA160342

出版年：

2019

DOI：

10.1049/cje.2018.12.001

中国电子杂志(英文版)

CSTPCDCSCDSCIEI

ISSN：1022-4653

年,卷(期)：2019.28(2)

参考文献量20