Urban land cover classification is not easy for its inner complexity resulting from human’s impact.The emergency of very high resolution remote sensing images makes it possible to classify the urban land cover for its ample details.The paper is about urban land cover classification method using multi-scale and multi-variable image segmentation method.The innovation of this method lies in selection of proper scale parameter resulting from proper image data and certain classification order.Normalized Digital Surface Model（nDSM） is constructed in the process of building extraction;the threshold of elevation difference is used to extract building.Accuracy assessment result indicates that the ideal urban land cover classification results have been obtained using this method.The total accuracy has increased from 83.04% to 88.52% compared with traditional object oriented classification result.The product accuracy of building increases from 60.27% to 93.91%

which is the highest among all the six objects.