Change detection based on similarity measurement of object histogram using high-resolution remote sensing imagery

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.