Abstract: A change detection method for high-resolution remotely sensed imagery is designed by combined the texture features with spectral features.The method can decrease change detection errors caused by building projection difference between two period imageries.It utilizes transform divergence between change and no-change to establish change detection strategy.Firstly,the differential spectral features are modeled by using Gaussian mixture model(GMM) and initial change area is obtained by using MRF minimum error probability.But for high-resolution imagery,the projection difference will disturb change area's result.For decreasing these errors,the texture features are extracted by multi-channel Gabor filter.When using these texture features,it is necessary to reduce these texture features' correlation and relieve computation burden.A class-within PCA is adopted to select texture features.Using selected texture features,the initial change area is modeled by using GMM and "false change" is got rid of using MRF minimum error probability.The experiment has shows that the mentioned method can remove false change caused by projection difference and improve change detection accuracy.