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 ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Zhou, Huiyu;
    Item type:Article
    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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