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A novel semisupervised support vector machine classifier based on active learning and context information

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Abstract

This paper proposes a novel semisupervised support vector machine classifier \((\hbox {S}^{3}\hbox {VM})\) based on active learning (AL) and context information to solve the problem where the number of labeled samples is insufficient. Firstly, a new semisupervised learning method is designed using AL to select unlabeled samples as the semilabled samples, then the context information is exploited to further expand the selected samples and relabel them, along with the labeled samples train \(\hbox {S}^{3}\hbox {VM}\) classifier. Next, a new query function is designed to enhance the reliability of the classification results by using the Euclidean distance between the samples. Finally, in order to enhance the robustness of the proposed algorithm, a fusion method is designed. Several experiments on change detection are performed by considering some real remote sensing images. The results show that the proposed algorithm in comparison with other algorithms can significantly improve the detection accuracy and achieve a fast convergence in addition to verify the effectiveness of the fusion method developed in this paper.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, China

    Fei Gao, Wenchao Lv, Yaotian Zhang, Jinping Sun & Jun Wang

  2. Space Mechatronic Systems Technology Laboratory, University of Strathclyde, Glasgow, G1 1XJ, UK

    Erfu Yang

Authors
  1. Fei Gao
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  2. Wenchao Lv
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  3. Yaotian Zhang
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  4. Jinping Sun
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  5. Jun Wang
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  6. Erfu Yang
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Corresponding author

Correspondence to Yaotian Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China (61071139; 61471019; 61171122; 61501011), the Aeronautical Science Foundation of China (20142051022), the Pre-research Project(9140A07040515HK01009), the National Natural Science Foundation of China (NNSFC) under the RSE-NNSFC Joint Project (2012-2014) (61211130210) with Beihang University, and the RSE-NNSFC Joint Project (2012-2014) (61211130309) with Anhui University.

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Gao, F., Lv, W., Zhang, Y. et al. A novel semisupervised support vector machine classifier based on active learning and context information. Multidim Syst Sign Process 27, 969–988 (2016). https://doi.org/10.1007/s11045-016-0396-1

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