Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images

Non-Gaussian triplet Markov fields （TMF） model is suitable for dealing with multi-class segmentation of non-stationary and non-Gaussian synthetic aperture radar （SAN） images. Considering the complexity of the model and algorithm, as well as the requirement of real-time, and robust and efficient processing of SAR images, a fast algorithm based on TMF for unsupervised multi-class segmentation of SAR images is proposed in this paper. For the speckle noise in SAR images, numerical characteristic, threshold selection and QuadTree decomposition criterion are researched firstly. With the new method, a SAR image can quickly be mapped into an edge-based pixon-representation, which results in a coarse decomposition in smooth regions, and a fine decomposition in edges. Then by combining TMF model with the pixon-representation of SAR image, a new potential energy function of TMF based on pixon-representation is derived. Finally, the segmentation is finished by Bayesian maximum posterior mode （MPM）. The effectiveness of the fast TMF algorithm is demonstrated by applying it to simulated data and real SAR images.