SAR Image Segmentation with Unknown Number of Classes Based on Multiscale Curvelet Decomposition

To segment SAR image with unknown number of classes, a region- and multiscale-based segmentation method with unknown number of classes is proposed. The multiscale curvelet coefficients are obtained by decomposing SAR image using curvelet transform. Then every scale coefficients are reconstructed by inverse curvelet transform from coarse to finest scale, and multiscale decomposed image is obtained. On this basis, an image domain is partitioned into a set of blocks by regular tessellation. Gamma distribution is used to build region characteristic field and Markov Random Field (MRF) is used to build region label field. The number of classes is considered as a random variable and subject to a Poisson distribution. Further, the Bayesian paradigm is followed to combine them to build the region- and multiscale-based segmentation method with unknown number of classes. Finally, a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate the model. In the processing of simulation, the segmentation result of current scale is regarded as the initial segmentation of next scale. And so on, until it gets to the finest scale, the corresponding segmentation is the final image segmentation. The proposed approach is used to segment simulated and real SAR images, and the results point out the feasibility and effectiveness of the proposed approach.