Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/43835
Title: A Novel Separability Objective Function in CNN for Feature Extraction of SAR Images
Authors: Gao, Fei
Wang, Meng
Wang, Jun
Yang, Erfu
Zhou, Huiyu
First Published: 1-Mar-2019
Publisher: Chinese Institute of Electronics
Citation: Chinese Journal of Electronics, 2019, 28(2), pp. 423 – 429
Abstract: 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.
DOI Link: 10.1049/cje.2018.12.001
ISSN: 1022-4653
eISSN: 2075-5597
Links: https://digital-library.theiet.org/content/journals/10.1049/cje.2018.12.001
http://hdl.handle.net/2381/43835
Embargo on file until: 1-Jan-10000
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2019, Chinese Institute of Electronics. Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Description: 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.
Appears in Collections:Published Articles, Dept. of Computer Science

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