Volume 8 Issue 4
Aug.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(4): 458-470
HUA Wenqiang, WANG Shuang, GUO Yanhe, et al. Semi-supervised PolSAR image classification based on the neighborhood minimum spanning tree[J]. Journal of Radars, 2019, 8(4): 458–470. doi: 10.12000/JR18104
Citation: HUA Wenqiang, WANG Shuang, GUO Yanhe, et al. Semi-supervised PolSAR image classification based on the neighborhood minimum spanning tree[J]. Journal of Radars, 2019, 8(4): 458–470. doi: 10.12000/JR18104
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Semi-supervised PolSAR Image Classification Based on the Neighborhood Minimum Spanning Tree

DOI: 10.12000/JR18104
  • ①.

    School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

  • ②.

    Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

  • ③.

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, 710071, China

Funds:  The National Natural Science Foundation of China (61771379), Shaanxi Key Disciplines of Special Funds Projects
More Information
  • Corresponding author: HUA Wenqiang, huawenqiang2013@163.com
  • Received Date: 2018-12-03
  • Rev Recd Date: 2018-12-28
    • Available Online: 2019-02-19
  • Publish Date: 2019-08-28
    Abstract
  • In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree (NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.

     

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    • References(27)
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