Volume 4 Issue 1
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Article Navigation >  Journal of Radars  > 2015  >  4(1): 93-98
Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
Citation: Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
shu

Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

doi: 10.12000/JR14138
  • 1.

    (Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, China)

  • 2.

    (International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an 710071, China)

  • Received Date: 2014-11-20
  • Rev Recd Date: 2015-02-28
  • Publish Date: 2015-02-28
    Abstract
  • In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM). Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

     

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