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

      1 , 1 , 1
      Journal of Radars
      Chinese Academy of Sciences
      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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          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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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 February 2015
          : 4
          : 1
          : 93-98
          Affiliations
          [1 ] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education
          Article
          6e7a7f0b1d5542c3bc38a834e0968f1e
          10.12000/JR14138
          ad1e6ead-dbcc-4154-a86f-8b959e783ff7

          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

          Remote sensing,Electrical engineering
          it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.,a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study,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,this method extracts multiple scattering features using target decomposition approach. Then,considering the scattering characters of PolSAR data,aiming at handling the issue that the number of train set is small. First,we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images,In this study

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