A novel active semisupervised convolutional neural network algorithm for SAR image recognition

Gao, Fei and Yue, Zhenyu and Wang, Jun and Sun, Jinping and Yang, Erfu and Zhou, Huiyu (2017) A novel active semisupervised convolutional neural network algorithm for SAR image recognition. Computational Intelligence and Neuroscience, 2017. pp. 1-8. 3105053. ISSN 1687-5273

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    Abstract

    Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.

    Creators(s): Gao, Fei, Yue, Zhenyu, Wang, Jun, Sun, Jinping, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Zhou, Huiyu;
    Item type:Article
    ID code:62902
    Keywords:convolutional neural networks, synthetic aperture radar, machine learning, image recognition, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:16 Jan 2018 15:18
    Last modified:09 Oct 2020 04:15
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62902
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