The object-oriented change vector analysis method

which is excessively dependent on the mean value of each object but failed to use gray distribution information

is deficient in change detection using high-resolution remote sensing images. A new method introducing similarity measurement of object histogram is proposed in this study. First

the similarity measurement of o bjects between different periods is built up by G statistic. Second

the Expectation Maximization（ EM） algorithm is used to calculate the related parameters according to the assumption that all similarity measurement values of objects fit a Gaussian Mixture D istribution model. Finally

the Bayesian rule with minimum error rate is applied to get the change / no change results. Experimental results show that the method can get results with higher accuracy in change detection

especially for high-resolution remote sensing images.