Multi-temporal remotely sensed imagery change detection is a hot topic in recent years.Most researchers pay attention to statistical pattern recognition principal to solve the problem.In this paper

we discuss the problem from three aspects.Firstly we focus on GMM model statistic coefficients resolve method.The Expectation Maximization（EM）is the most commonly method to calculate GMM coefficients.However

EM algorithm often converges to local value.So we combine genetic k-means algorithm（GKA）with EM to modify it.Using the initial clustering result obtained by GKA

we are able to initialize EM globally.The combination helps EM search out globally optimal solution and enhances the automatic degree.When we get the global optimal results

it is easy for us to obtain change detection result using Bayes Rule for Minimum Error（BRME）.However

the BRME doesn’t take into account the image’s contextual information.It is well known that one pixel belongs to "change" or "no change" depends not only on itself

but also on its neighbor pixels.There are two ways to model contextual information.The first is probability relaxation iteration

and the second is Markov Random Field（MRF）.MRF has two commonly used solution methods;one is Iterated Conditional Method（ICM）

and the other is Simulated Annealing（SA）.In this paper

we compare the three spatial contextual change detection methods using visual effect and kappa coefficient.The experiment shows that MRF based on simulated annealing has better performance than the other two.Through the above experiments

we find that traditional MRF deals all pixels equally and ignores the neighbor local features.In fact

in different region

the spatial information has different impact.We analyze the impact and propose variable weight MRF method.It can adaptively vary spatial impact according to different image local features.It has virtues of preserving structural change and filter noises.The experiment proves that variable weight MRF gets the best result.