Novel semi-supervised classification method based on class certainty of samples

Gao, Fei and Yue, Zhenyu and Wang, Jun and Yang, Erfu and Hussain, Amir (2018) Novel semi-supervised classification method based on class certainty of samples. In: The 9th International Conference on Brain-Inspired Cognitive System​, 2018-07-07 - 2018-07-08, Guangcheng Hotel.

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    Abstract

    The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labeled samples. However, the number of labeled samples is limited due to the expensive and time-consuming collection. To effectively utilize the information of unlabeled samples in the learning process, this paper proposes a novel semi-supervised classification method based on class certainty of samples (CCS). First, the class certainty of unlabeled samples obtained based on multi-class SVM is smoothed for robustness. Then, a new semi-supervised linear discriminant analysis (LDA) is presented based on class certainty, which improves the separability of samples in the projection subspace. Ultimately, we extend the semi-supervised LDA to nonlinear dimensional reduction by combining class certainty and kernel methods. Furthermore, to assess the effectiveness of proposed method, the nearest neighbor classifier is adopted to classify actual SAR images. The results demonstrate that the proposed method can effectively exploit the information of unlabeled samples and greatly improve the classification effect compared with other state-of-the-art approaches.

    ORCID iDs

    Gao, Fei, Yue, Zhenyu, Wang, Jun, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Hussain, Amir;
    • Item type:Conference or Workshop Item(Paper)
      ID code:64732
      Dates:
      Date
      Event
      7 July 2018
      Published
      16 June 2018
      Accepted
      Keywords:remote sensing images, semi-supervised classification, class certainty, semi-supervised LDA, kernal method, Technology, Engineering(all)
      Subjects:Technology
      Department:Faculty of Engineering > Design, Manufacture and Engineering Management
      Depositing user: Pure Administrator
      Date deposited:09 Jul 2018 13:24
      Last modified:25 Jul 2021 02:25
      URI:https://strathprints.strath.ac.uk/id/eprint/64732
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