A maximum likelihood estimation method for multispectral autoregressive image models

We consider the problem of maximum likelihood estimation applied to multispectral random field image models, specifically the multispectral simultaneous autoregressive (MSAR) model. Although previous work has provided least squares methods for parameter estimation, the maximum likelihood method often produces better results. For images with an assumed Gaussian distribution we develop effective procedures for calculating these estimates. Through a series of experiments using known random field models and natural texture samples, the effectiveness of the maximum likelihood approach is demonstrated.