Image segmentation has been a hot topic in image processing.It involves two tasks:determining the number of homogeneous regions and segmenting them.Most image segmentation algorithms mainly focus on the latter and determine a priori the number.Artificially determining the number of classes is difficult for certain reasons.Consequently

the number should be automatically determined.A statistical and region-based segmentation approach for color remote-sensing images is introduced in this paper.First

the image domain is partitioned into groups of regular sub-regions（or blocks） by regular tessellation.Second

the image is modeled on the assumption that the intensities of its pixels in each homogeneous region follow an identical and independent multivariate Gaussian distribution.The Bayesian paradigm is applied to establish the image segmentation model.Third

a reversible-jump Markov chain Monte Carlo（RJMCMC）scheme is designed to simulate the segmentation model

which determines the number of classes and roughly segments the image.A refined operation is performed to improve the accuracy of the image segmentation results further.Real and synthetic color remote-sensing images from Worldview-Ⅱ and multispectral IKONOS images are tested.Qualitative and quantitative evaluation results are obtained to verify the feasibility and effectiveness of the proposed method.The proposed method exhibits advantages over two other segmentation methods

namely

the ISODATA method and the segmentation method combining Voronoi tessellation with EM/MPM algorithm.An image segmentation approach based on regular tessellation and RJMCMC algorithm is proposed in this study.The proposed approach can not only automatically determine the number of classes but also segment homogenous regions better.Furthermore

the test results also show that the approach demonstrates high performance and high efficiency.In future studies

other tessellation methods can be used to partition the image domain.