Unsupervised Classification of Polarimetric SAR Images by Gamma-Correction of Features Using Self Organizing Map

In this paper, an unsupervised method isproposed for target classification in a polarimetric SARimage, based on the Gamma correction and the Self organizing map (SOM). After the gamma correction of thefeatures including the elements of the coherency matrixand its eigenvalues, the coeffcients of Freeman's decomposition and the polarization entropy, the authors use a SOMbased neural network to classify a polarimetric SAR imageinto different clusters. Using the AirSAR data, the authorsdemonstrate the effectiveness of the proposed method.