The intensities of pixels in images of multi-look Synthetic Aperture Radar（SAR） are usually modeled by gamma distribution.To obtain a segmentation result

the parameters that indicate the distributions should be estimated.However

traditional expectation maximization（EM） algorithm cannot approach the shape parameter through taking partial derivation of the likelihood probability.Thus

to solve this problem

the shape parameter should be made equal to the number of looks

and only the scale parameter should be estimated.Nevertheless

the conclusion is obtained under the assumption that a pixel is constructed by infinite scattering units

which is impossible especially in highresolution SAR images.Apparently

that assumption is unreasonable and will lead to inaccurate estimation of the corresponding parameters.Moreover

gamma distribution with fixed-shape parameter cannot completely describe the characteristics of intensity distribution in multilook SAR images.Thus

this paper proposes a scheme for solving the parameter based on Expectation/Conditional Maximization（ECM） algorithm and develops a new segmentation algorithm for multi-look SAR images.First

the neighborhood system on the label field is considered

and Markov random field model is employed to define prior distribution.Then

intensities in each homogeneous region of a multilook SAR image are modeled by gamma distribution

in which both shape and scale parameters are considered as variables.Finally

the gamma distribution along with the prior distribution constructs the posterior distribution

on which purpose it should be maximized.In this paper

Metropolis-Hastings algorithm is used as a random sampling method in estimating the marginal posterior probability model and changing the labels of each pixel.However

the estimation of the shape parameter remains a problem because it cannot be solved through partial derivation.ECM algorithm introduces the Newton iterative method to approach the real value of parameters

which cannot be directly solved from an equation.Therefore

shape parameter is evaluated by ECM algorithm

and all variables are obtained ultimately.The proposed algorithm is applied to simulated and real multi-look SAR images.Experimental results show that the proposed method can accurately estimate shape and scale parameters of gamma distribution.The proposed algorithm’s user

product

and total accuracies

and kappa coefficient are all higher than those of the EM algorithm’s

and the estimated values of the algorithm are much closer to the real values.The proposed algorithm can estimate the parameters of gamma distribution and the corresponding reality of the label field under the circumstance of maximized posteriori probability.Experiments on simulated and real multi-look SAR image verify the effectiveness and feasibility of the proposed algorithm

and the shape and scale parameters of gamma distribution can quickly converge to their stable state in a considerably short time.The ECM algorithm introduced in this paper can estimate the value of the shape parameter in gamma distribution

which is treated as the number of looks in the traditional EM algorithm.Thus

the connections between different looks are included in the distribution

and the corresponding segmentation results are improved.