Volume 8 Issue 4
Aug.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(4): 448-457
ZHANG Lamei, ZHANG Siyu, DONG Hongwei, et al. Robust classification of PolSAR images based on Pinball loss support vector machine[J]. Journal of Radars, 2019, 8(4): 448–457. doi: 10.12000/JR19055
Citation: ZHANG Lamei, ZHANG Siyu, DONG Hongwei, et al. Robust classification of PolSAR images based on Pinball loss support vector machine[J]. Journal of Radars, 2019, 8(4): 448–457. doi: 10.12000/JR19055
shu

Robust Classification of PolSAR Images Based on Pinball loss Support Vector Machine

doi: 10.12000/JR19055
  • ①.

    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

  • ②.

    Institute of Beijing Remote Sensing Information, Beijing 100192, China

Funds:  The National Natural Science Foundation of China (61401124, 61871158), The Aeronautical Science Foundation of China (20182077008), The Scientific Research Foundation for the Returned Overseas Scholars of Heilongjiang Province (LC2018029)
More Information
  • Corresponding author: ZHU Xia, nudt_zs@163.com
  • Received Date: 2019-05-01
  • Rev Recd Date: 2019-07-12
    • Available Online: 2019-07-26
  • Publish Date: 2019-08-28
    Abstract
  • Given the problems that the amount of supervised information in the Polarimetric Synthetic Aperture Radar (PolSAR) image is low and the speckle noise is difficult to eliminate, in this study, a robust classification algorithm for PolSAR image based on Pinball loss Support Vector Machine (Pin-SVM) is proposed from the perspective of robust statistical learning. On the basis of the scattering characteristics of PolSAR images and the texture characteristics of surface features, the proposed algorithm determines the optimal decision hyperplane by solving the maximum quantile distance between the samples of two classes, which can provide more robust results without iteration. Compared with the traditional PolSAR image classification algorithms that solve the maximum margin, on one hand, the proposed algorithm is robust to the noise contained in the features extracted from PolSAR images. On the other hand, the proposed algorithm is insensitive to the sampling range of training samples, which means that it has better robustness to resampling. The experimental results of EMISAR-Foulum PolSAR data prove the validity of the proposed algorithm through comparative tests in a variety of situations.

     

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