Using similarity parameters for supervised polarimetric SAR image classification

Junyi Xu, Jian Yang, Yingning Peng, Chao Wang, Yuei An Liou

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In this paper, a new method is proposed for supervised classification of ground cover types by using polarimetric synthetic aperture radar (SAR) data. The concept of similarity parameter between two scattering matrices is introduced for characterizing target scattering mechanism. Four similarity parameters of each pixel in image are used for classification. They are the similarity parameters between a pixel and a plane, a dihedral, a helix and a wire. The total received power of each pixel is also used since the similarity parameter is independent of the spans of target scattering matrices. The supervised classification is carried out based on the principal component analysis. This analysis is applied to each data set in image in the feature space for getting the corresponding feature transform vector. The inner product of two vectors is used as a distance measure in classification. The classification result of the new scheme is shown and it is compared to the results of principal component analysis with other decomposition coefficients, to demonstrate the effectiveness of the similarity parameters.

Original languageEnglish
Pages (from-to)2934-2942
Number of pages9
JournalIEICE Transactions on Communications
Issue number12
StatePublished - Dec 2002


  • Classification
  • Principal component analysis
  • Radar polarimetry
  • Similarity parameter
  • Synthetic aperture radar


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