This paper takes fully polarimetric SAR data as study object to analyze the change detection technology. As the coherency matrix C3 or covariance matrix T3 of fully polarimetric SAR data follows a complex Wishart distribution. First

the likelihood-ratio parameter is built by a test statistic based on Wishart distribution to represent change features. Then the initial change information is extracted by the expectation maximization （EM） iterative algorithm based on a general Gaussian distribution. Finally

the change information is generated by optimizing the initial change information using probability relaxation iteration algorithm considering the context information. The method can extract change information automatically as well as produce change result of almost speckle noise free by integrating a series of filtering operations

including incoherent average

initial classification

optimizing classification. The validity of the method is demonstrated by comparison with traditional logarithm ratio method.