A novel separability objective function in CNN for feature extraction of SAR images
Gao, Fei and Wang, Meng and Wang, Jun and Yang, Erfu and Zhou, Huiyu (2019) A novel separability objective function in CNN for feature extraction of SAR images. Chinese Journal of Electronics, 28 (2). pp. 423-429. ISSN 1022-4653
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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.
Creators(s): |
Gao, Fei, Wang, Meng, Wang, Jun, Yang, Erfu ![]() | Item type: | Article |
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ID code: | 72084 |
Keywords: | classification, convolution neural network (CNN), linear separability, objective function, synthetic aperture radar (SAR), Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering, Applied Mathematics |
Subjects: | Technology > Electrical engineering. Electronics Nuclear engineering |
Department: | Faculty of Engineering > Design, Manufacture and Engineering Management |
Depositing user: | Pure Administrator |
Date deposited: | 17 Apr 2020 12:10 |
Last modified: | 21 Jan 2021 11:02 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/72084 |
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