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

journal contribution
posted on 17.04.2019, 14:38 by Fei Gao, Meng Wang, Jun Wang, Erfu Yang, Huiyu Zhou
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.

History

Citation

Chinese Journal of Electronics, 2019, 28(2), pp. 423 – 429

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

AM (Accepted Manuscript)

Published in

Chinese Journal of Electronics

Publisher

Chinese Institute of Electronics

issn

1022-4653

eissn

2075-5597

Acceptance date

11/08/2018

Copyright date

2019

Publisher version

https://digital-library.theiet.org/content/journals/10.1049/cje.2018.12.001

Notes

The file associated with this record is under a permanent embargo in accordance with the publisher's policy. The full text may be available through the publisher links provided above.

Language

en

Exports