As human activities expanding

land use and land cover change very quickly at different scales all over the world. Remote sensing becomes a major tool to acquire information of LUCC. In recent years

the continuing development of remote sensing technology provides us a large amount of remote sensing data at high spatial

spectral and temporal resolutions. Advances in remote sensing science and diversity in high resolution data hold great promise for improving the precision of information extraction and change detection

which also make change detection of land use and land cover at different scales from global scale to local scale more difficult. However

conventional remote sensing change detection techniques are inefficient due to the high spatial heterogeneity of inner objects in the image

more texture

more details and clear edges. Moreover

the requirement for real time and effective change detection methods and large size of high spatial resolution imagery cells for development of more automatic techniques of change detection. The method of change detection based on integration of change vector analysis and similarity calibration is presented for high spatial resolution data. It can be used to detect the change of building and street quickly and automatically. In this paper

we present details of the method of change object extraction and verification The methods are illustrated with an airborne linear scanner sensor image over the suburb of Tokyo city

Japan. The result of change detection will be compatible to complexity and fuzzy degree of change of object in high spatial resolution imagery at different times

which is distinguishable to the results using conventional change detection

in which the result only provide "change" and "no-change". The experimental results suggest that change detection based on object similarity calibration is more reliable

efficient than post classification change detection using high spatial resolution imagery.