Volume 5 Issue 6
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Article Navigation >  Journal of Radars  > 2016  >  5(6): 692-700
Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning[J]. Journal of Radars, 2016, 5(6): 692-700. doi: 10.12000/JR15132
Citation: Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning[J]. Journal of Radars, 2016, 5(6): 692-700. doi: 10.12000/JR15132
shu

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

DOI: 10.12000/JR15132
  • 1.

    (School of Electronic Information, Wuhan University, Wuhan 430072, China)

  • 2.

    (Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)

  • 3.

    (Shanghai Institute of Satellite Engineering, Shanghai 200240, China)

Funds:

The Inner Mongolia Autonomous Region Science and Technology Project (20131108,20140155),TheNational Natural Science Foundation of China (61271401,41501414),The Fudan University Key Laboratory of EMWInformation Open Fund Project (EMW201504)

  • Received Date: 2015-12-27
  • Rev Recd Date: 2016-04-07
  • Publish Date: 2016-12-28
    Abstract
  • In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

     

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