Spectral clustering methods have recen tly shown great promise for the problem of image segmentation.However,the comput ational demands of these approaches make them infeasible to large problems such as multispectral remote sensing images.According to the particularities of multi spectral remote sensing images,a segmentation method is proposed based on spectr al clustering,statistics characteristics of Contourlet coefficients and multisca le Markov model.First,the spectral clustering is implemented to the coarsest low pass image of multispectral remote sensing image in Contourlet domain to obtain the initial segmentation result;Second,a multiscale Markov model,which contains the initial segmentation result as the coarsest scale,is constructed using mutua l information to capture the relationship between Contourlet coefficients in the same scale and across scales;Third,the final segmentation result is obtained by confusing multiscale and multi-directional image information based on the mult iscale Markov model.Compared with the classical spectral clustering method and t he multiscale segmentation method based on HMT model in Contourlet domain,the se gmentation results for both synthetic images and real multispectral remote sensi ng images show that the proposed method not only has better performance in edges preservation and noise sensitivity,but also has lower misclassification probabi lity and running time,and it can achieve satisfactory segmentation results.