Abstract: Objective This paper proposes a geographical object-based method for change detection with high reso-lution images based on the changing areas distributed as a clustered type.This algorithm utilizes theMean-Shift segmentation algorithm to extract a geographic object,and then uses the gray informationof the geographic object with the EM algorithm to automatically extract changed and unchanged areas.This method considers spatial neighborhood information which can avoid the isolation and discrete dis-connected areas in change results when using apixel-based method.This method also reduces inter-vention when determining the change threshold value.Groups of three different spatial resolution ima-ges(QuickBird,SPOT,TM images)are used to verify this proposed geographic object-based changedetection algorithm and compared the accuracy and precision with a pixel-base method.Our resultsshow that the accuracy with object-based change detection method on QuickBird,SPOT and TM ima-ges was 91.1%,87.3% and 84.3%,while for the pixel-based method are 86.41%,82.48% and81.02%respectively.These results illustrate that the object-based change detection method has high-er change detection accuracy than the pixel based approach.Moreover,the object-based method hasbetter accuracy for high spatial resolution than in middle or low resolution images.