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

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          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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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 December 2016
          : 5
          : 6
          : 692-700
          Affiliations
          [1 ] ①(School of Electronic Information, Wuhan University, Wuhan 430072, China)
          [2 ] ②(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)
          [3 ] ③(Shanghai Institute of Satellite Engineering, Shanghai 200240, China)
          Article
          3a2467175c1d4aac86298c6c9ca66dcb
          10.12000/JR15132

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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