AN UNSUPERVISED HIDDEN MARKOV RANDOM FIELD BASED SEGMENTATION OF POLARIMETRIC SAR IMAGES

This paper proposes an iterative unsupervised Markov Random Field (MRF) based segmentation technique for polarimetric Synthetic Aperture Radar (SAR) image using the optimized scattering mechanism similarity parameters. Parameter estimation for the MRF model is generally performed from the available training data in order to perform tasks including semantic image segmentation. Since the current scenario is entirely unsupervised, the parameter estimation is performed iteratively using the Expectation Maximization (EM) technique considering the classes are distributed according to Gaussian functions. Further, we model the pairwise potential of the MRF cost function using a weighted combination of the similarity parameters. Results obtained on a fully polarimetric SAR data establishes the potential of such unsupervised random field models for analyzing SAR data effectively.