Generation of pseudo-fully polarimetric data from dual polarimetric data for land cover classification

A linear relationship among the HH, HV, and VV components of polarimetric synthetic aperture radar (SAR) data is studied. A regression model was developed to predict the real and imaginary parts of the VV polarimetric component from the HH and HV components in dual polarimetric SAR and the resulting dataset is called pseudo-fully polarimetric SAR data. Freeman-Wishart classification was applied to evaluate the preservation of scattering characteristics in the pseudo-fully polarimetric dataset. A kappa coefficient is 0.81 indicates very good agreement between the two classification results. An SVM was used for the land cover classification. Finally, post-processing was implemented to remove noise in the form of isolated pixels. A VNIR-2 optical data taken over the same area at nearly same time was used as ground truth data to assess the classification accuracy. The land cover classification result obtained from the SVM shows that using the pseudo-fully polarimetric data gives more than a 2% improvement of mean producer's accuracy over dual polarimetric datasets.