The speckle appearing on SAR images is a natural phenomenon generated by the coherent processing of radar back scattering. The presence of speckle not only reduces the interpreter's ability to resolve fine details but also hinders the automatic segmentation of such images. In remote sensing image analysis

the spatial structure hidden in an image may contain even more information than the pixel itself

particularly for SAR images

where the large variability owing to the speckle noise makes the single pixel value unreliable. In particular

Markov random field (MRF) presents many interesting properties to model the relationship among neighbor pixels

which not only allows the creation of segmentation techniques that are able to improve segmented results in a statistically optimal manner but also provide an efficient regularization method

which is necessary because image segmentation is an inverse problem that is generally ill-posed from a mathematical point of view. However

MRF-based spatial dependency models lead to an extremely large amount of computations for the estimation of model parameters with a standard expectation-maximization (EM) algorithm. Moreover

the EM-based model parameter estimation is performed over the whole image and does not reflect local image properties. To improve MRF-EM image segmentation

this paper proposes the identification of the global MRF segmentation model by distributing a set of EM procedures within a multi-agent framework. The image is segmented into situated agents

which estimate the parameters of an MRF model. Local features are changed by a cooperation agent with genetic algorithms (GA). To address a statistical approach to SAR intensity image

each pixel in the image is assigned a label to indicate the homogeneous region to which it belongs depending on the statistical properties of the pixel and its neighbors. The individual labels form a so-called label field

in which labels are modeled with a prior Potts model where labels of neighboring pixels will tend to be similar. In each homogeneous region

the pixels are modeled with Gamma distribution with parameters that are dependent on the type of region. By Bayesian theory

the segmentation model can be built by producing the image model and label model. To simulate the segmentation model

an approach combining EM algorithm and GA within a multi-agent system (MAS) framework is proposed. The MAS is composed of a series of segmentation agents and a cooperation agent. Each segmentation agent initializes a global segmentation by EM algorithm

and the cooperation agent employs GA to implement global optimal segmentation. The parameters of the model are estimated by E-and M-steps

and global segmentation is also completed simultaneously. GA attempts to obtain an optimal segmentation by genetically breeding a population of individuals obtained by the segmentation agents. Each individual is encoded by the label field as a chromosome

and each chromosome defines a measure of fitness built by the segmentation model. Fitness can be applied to determine the ability of the chromosome to survive and to produce an off-spring. To obtain optimal segmentation

the selection operator directly inherits individuals from parents by roulette

and the crossover operator exchanges some pixels from pairs of parents to produce new off-springs.Thereafter

the mutation operator changes certain site labels

which are randomly selected and replaced with another category depending on its neighborhoods. Experiments are conducted with simulated and real SAR intensity images by the proposed method and EM algorithm

respectively. Results show that the proposed methodhas better image segmentation performance than the EM method. The problems of edge blur and segmentation mistake in homogeneous regions are solved by combining the distributed EM procedures and genetic operators. The selection operator diversifies the segmentation results

and the crossover process performs global exchanges.Furthermore

the mutation process ensures that the agents can escape from a local point. Therefore

the global optimal solution can be obtained. To quantitatively analyze this method

the overall accuracy and Kappa coefficient of the producers and users are calculated from the confusion matrix and are compared with those of the EM algorithm. Result shows that the presented approach is more accurate than the EM algorithm. All experiments demonstrate the robustness and veracity of the proposed approach. This paper presents a distributed segmentation approach that combines the EM algorithm and GA within the MAS framework. The experiment results indicate that the proposed approach is effective and promising. Although this method produces excellent results

efficiency still needs improvement.Furthermore

MAS has many functions that can be used for segmentation. In the future

our research will focus on the definition of each agent and the interaction among agents (e.g.

communication).