Entropy-based classification followed by unsupervised and supervised Wishart classifications

The aim of this study is to classify the Pi-SAR (polarimetric and interferometric synthetic aperture radar) data for urban land cover mapping using advanced polarimetric classification methods. For the actual classifications, entropy-based method followed by unsupervised and supervised Wishart maximum likelihood classification (MLC) are used. The performances of the unsupervised and supervised methods are compared in terms of discrimination of different urban land cover types.