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Volume 29 Issue 1
Jan.  2011
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Wu Yong-hui, Ji Ke-feng, Yu Wen-xian. Unsupervised Classification of Fully Polarimetric SAR Image Using H- Decomposition and Modified C-Mean Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(1): 30-34. doi: 10.3724/SP.J.1146.2005.00635
Citation: Wu Yong-hui, Ji Ke-feng, Yu Wen-xian. Unsupervised Classification of Fully Polarimetric SAR Image Using H- Decomposition and Modified C-Mean Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(1): 30-34. doi: 10.3724/SP.J.1146.2005.00635

Unsupervised Classification of Fully Polarimetric SAR Image Using H- Decomposition and Modified C-Mean Algorithm

doi: 10.3724/SP.J.1146.2005.00635
  • Received Date: 2005-06-06
  • Rev Recd Date: 2005-11-28
  • Publish Date: 2007-01-19
  • A new method for unsupervised classification of terrain types using fully POLarimetric Synthetic Aperture Radar (POLSAR) data is proposed in this paper. The method is a combination of the unsupervised classification based on Cloudes H- decomposition and the modified C-mean algorithm. The fully polarimetric SAR image is initially classified using Cloudes method. The classification map is used as input of the modified C-mean algorithm, and then iteration is performed. It is important to determine the number of iteration in the modified C-mean algorithm, and a new termination criterion is presented using image entropy to do so. Compared with H- decomposition, not only scattering mechanisms of all classes can be preserved, but also terrain classification is effectively performed using this method. The effectiveness of this method is demonstrated using an L-band fully polarimetric SAR image of San Francisco, acquired by the NASA/JPL AIRSAR sensor.
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