Abstract:  Based upon the premise of ensuring the complexity and accuracy of remote sensing image segmentation model, it is crucial to automatically determine the number of segmentation categories. An image segmentation algorithm is constructed in this paper by combining reversible jump Markov Chain Monte Carlo and simulated annealing (RJMCMC+SA). The image is smoothed geometrically by Gauss curvature filtering (GC), and the posterior probability distribution is established by formalizing the parameters in the nonlinear regression model based on Bayesian theory. And then RJMCMC algorithm is used to accomplish the posterior probability distribution and construct the probability transfer core. After that SA algorithm is used to accelerate the convergence of the probability transfer kernel to determine the number and parameters of the radial basis function in the segmentation algorithm and complete the automatic determination of the number of categories and image global segmentation. Finally, the segmentation algorithm is compared with four RBF segmentation models in panchromatic remote sensing images and Berkeley University experimental database images. The data analysis shows that the algorithm not only strikes a good balance in complexity and accuracy, but also automatically determines the number of image categories.

Key words: Gauss curvature filtering, posterior probability, RJMCMC+SA, model selection method, confusion matrix