A fast automatic U-distribution segmentation algorithm for polsar images

A novel unsupervised, non-Guassian and contextual clustering algorithm for segmentation of polarimetric SAR images has been presented in [1]. This represents one of the most advanced PolSAR unsupervised statistical segmentation algorithm and uses the doubly flexible, two parameter, U-distribution model for the PolSAR statistics. However complexity of the probability density function leads to high time consumption. This paper investigate the key dependent variable in the U-distribution model and find a new parameter domain where the PDFs are smooth. Then a one-dimensional look-up table is set in this domain with nodes number determined by corresponding Fourier spectrum and is adopted to avoid re-evaluating the numerical integral in PDF to calculate class posteriori probabilities for every sample. The proposed strategy is incorporated in the standard segmentation algorithm. Prototype test has been carried out to validate the effectiveness of the proposed method.