Texture plays an important role in the composition of natural images and its analysis and classification are essential in a variety of image processing application.The method of texture analysis chosen for feature extraction is clearly critical to the success of the texture classification.Markov random fields（MRF） are a popular statistical model for textures.They capture local characteristics of an image by assuming a local conditional probability distribution.Many models used for gray level images have been proposed

but they cannot perform well in multispectral textured images.In this paper

a universal MRF model for multispectral textured images is developed

which take into account not only the spatial interaction within each of the multispectral bands

but also the interaction between different bands.As we all know

A MRF-based approach employs MRF model parameters as texture features to discriminate different textures.Because of the interaction between different bands

the universal MRF model is very complex

and estimating the corresponding parameters is very difficult.Therefore

in order to compute the universal MRF is parameters efficiently

a simplified equation using the maximum pseudo-likelihood method is built.After texture feature extraction

a supervised classification is applied to the original spectral bands combined with textural images.In this supervised classification system

the feature values are used by a Bayes classifier to make an initial probabilistic labeling.The spatial constraints are then enforced through the use of the Peleg’s Probabilistic relaxation algorithm.Necessary experiments are performed on samples of FG Forrest and QuickBird imagery

and the results indicate that the proposed algorithm provides better classification accuracy than other conventional approaches.