IMPLEMENTATION OF UNSUPERVISED CLASSIFICATION AND COMBINED CLASSIFICATION BASED ON H/q REGION DIVISION AND WISHART CLASSIFIER ON POLARIMETRIC SAR IMAGE

Speckles appearing in synthetic aperture radar (SAR) images are generated by the coherent processing of radar signals[1].This paper implements and analyze various types of speckle filtering methods.Based on filtering result, classification of the polarimetric SAR data is done by implementing unsupervised and combined classification.Unsupervised classification uses the backscattering power SPAN combined with H/q plane to obtain the initial cluster centers. Region segmentation is not very much accurate, combined classification is implemented.The combined classification uses H/q plane region division and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix [2].Experimental results with single look comlex polarimetric data from RISAT-1 demonstrate the effectiveness of these speckle filtering methods. Classification results based on unsupervised and combined classification are demonstrated using RISAT-1 polarimetric SAR data. Keywords—polarimetric entropy, alpha angle, q parameter, Radar polarimetry, synthetic aperture radar