Volume 8 Issue 4
Aug.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(4): 471-478
HU Tao, LI Weihua, QIN Xianxiang, et al. Terrain classification of polarimetric synthetic aperture radar images based on deep learning and conditional random field model[J]. Journal of Radars, 2019, 8(4): 471–478. doi: 10.12000/JR18065
Citation: HU Tao, LI Weihua, QIN Xianxiang, et al. Terrain classification of polarimetric synthetic aperture radar images based on deep learning and conditional random field model[J]. Journal of Radars, 2019, 8(4): 471–478. doi: 10.12000/JR18065
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Terrain Classification of Polarimetric Synthetic Aperture Radar Images Based on Deep Learning and Conditional Random Field Model

DOI: 10.12000/JR18065
  • ①.

    College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

  • ②.

    College of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China

Funds:  The National Natural Science Foundation of China (41601436, 61403414, 61703423), The Natural Science Foundation Research Project of Shaanxi Province (2018JM4029)
More Information
  • Corresponding author: QIN Xianxiang, qinxianxiang@126.com
  • Received Date: 2018-08-31
  • Rev Recd Date: 2018-12-26
  • Publish Date: 2019-08-28
    Abstract
  • In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.

     

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