Research Output
A Novel Semi-supervised Classification Method Based on Class Certainty of Samples
  The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labelled samples. However, the number of labelled samples is limited due to the expensive and time-consuming collection. To effectively utilize the information of unlabelled 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 unlabelled 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. Finally, the nearest neighbor classifier is adopted to classify the images. The experimental results demonstrate that the proposed method can effectively exploit the information of unlabelled samples and greatly improve the classification effect compared with other state-of-the-art approaches.

  • Date:

    06 October 2018

  • Publication Status:


  • DOI:


  • Funders:

    Engineering and Physical Sciences Research Council; National Natural Science Foundation of China


Gao, F., Yue, Z., Xiong, Q., Wang, J., Yang, E., & Hussain, A. (2018). A Novel Semi-supervised Classification Method Based on Class Certainty of Samples.



Remote sensing images, Semi-supervised classification, Class certainty, Semi-supervised LDA

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