Landscape change affects ecological functions and processes. Therefore, accurate information on landscape pattern and change is crucial for ecosystem monitoring and environmental change studies. To accurately quantify the landscape pattern and change, land use/land cover (LULC) classification and change analysis based on remote sensing are frequently applied. Recently, updating/backdating approaches have been increasingly used for the LULC classification and change analysis, but mostly based on pixels. Here, we present a new approach that integrates backdating with object-based image analysis, and compared it with the pixel-based backdating approach. We implemented these approaches in the Beijing metropolitan area, using a classification map for year 2009 as reference and backdating the map of 2001 and also analyzing the changes from 2001 to 2009. We further applied this approach for testing the efficiency by generating seven maps in the year of 1979, 1985, 1990, 1995, 2000, 2005, 2010 and 2015 in a rapidly urbanized area, Shenzhen in China. We found that: 1) an object-based backdating approach achieved higher accuracy for LULC classification and change analysis than the pixel-based backdating approach. With the object-based approach, the overall accuracies for the land use/cover classification and change analysis were 84.33% (versus 69.33% for a pixel-based approach), and 80.00% (versus 70.50% for a pixel-based approach), respectively. Such higher accuracies largely benefited from the object-based method, which provided an effective way in incorporating the spatial information and expert knowledge. 2) The object-based backdating approach greatly increased the efficiency because the classification and change analysis were only conducted for locations with changes. The increase in efficiency was particularly important for quantifying landscape pattern and change at a large area, for example, at the national or global scale.