Unsupervised Segmentation of Multilook Polarimetric Synthetic Aperture Radar Images
This paper proposes a new unsupervised image segmentation method for multilook polarimetric synthetic aperture radar (PolSAR) data. The statistical model for the PolSAR data is considered as a ď¬?nite mixture of non-Gaussian compound distributions considered as the product of two statistically independent random variates, speckle, and texture. With different texture distributions, the product model leads to various expressions of the compound distribution. The method uses a Markov random ď¬?eld (MRF) model for pixel class labels. The expectationâ??maximization/maximization of the posterior marginals (EM/MPM) algorithm is used for the simultaneous estimation of texture and speckle parameters and for the segmentation of multilook PolSAR images. Simulated and real PolSAR data are shown to demonstrate the method.
Expectationâ??maximization (EM) algorithm, Markov random ď¬?eld (MRF), maximum-likelihood (ML), maximization of the posterior marginals (MPM), polarimetric synthetic aperture radar (PolSAR).