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Volume 29 Issue 4
Jan.  2011
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Kang Xin, Han Chong-zhao, Xu Feng, Wang Ying-hua. Unsupervised Classification of Polarimetric SAR Image Using Deorientation Theory and Complex Wishart Distribution[J]. Journal of Electronics & Information Technology, 2007, 29(4): 822-826. doi: 10.3724/SP.J.1146.2006.00416
Citation: Kang Xin, Han Chong-zhao, Xu Feng, Wang Ying-hua. Unsupervised Classification of Polarimetric SAR Image Using Deorientation Theory and Complex Wishart Distribution[J]. Journal of Electronics & Information Technology, 2007, 29(4): 822-826. doi: 10.3724/SP.J.1146.2006.00416

Unsupervised Classification of Polarimetric SAR Image Using Deorientation Theory and Complex Wishart Distribution

doi: 10.3724/SP.J.1146.2006.00416
  • Received Date: 2006-04-03
  • Rev Recd Date: 2006-09-18
  • Publish Date: 2007-04-19
  • Scatter targets of complex terrain surfaces with random orientation product random fluctuating echoes. This leads to a confused classification by directly using target decomposition on full polarimetric SAR (PolSAR) image. To solve this problem, a new unsupervised classification method is proposed in this paper. Firstly, the target vector is transformed to the state with minimization of cross-polarization (min-x-pol); then the parameters u/v/H are used to characterized scattering mechanism, and the fuzzy membership is adopted instead of hard division of parameter plan; finally, characterizing the coherency matrix as multivariable complex Wishart distribution, the polarimetric SAR image is classified based on Bayes maximum likelihood criteria. Experiment is performed on a L-band NASA/JPL SIR-C polarimetric SAR image over Danshui town, Guangdong, P.R. China. Furthermore, the movements of the clustering centers are discussed. Compared with the k-mean like method based on H/, the results show that the proposed method provides a significant performance improvement in classification result and the associated scattering mechanism of class is more accurate. The classification result is beneficial for automatic recognition of terrain type.
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