针对单一应用遥感影像难以进行城市内部用地结构分类以及高精度城市内部用地多期空间数据叠置分析中位置误差问题建立了基于“分层分类”与“对象分割”的城市内部用地空间信息数字重建方法。实现对特大城市产业用地(住宅、商业、工业等)以及交通、水系、生态绿地等不同功能结构用地的高精度监测以及历史演变过程的重建。综合集成SPOT5, 1︰1万地形图、历史地图及城市规划图等辅助信息对长春城市1905年以来城市用地信息进行分类。研究表明, 在专家知识参与下人—机交互解译, 集成多源空间信息对实现高精度城市用地空间信息重建具有

Urban internal function and structure which is represented by intra-urban land use information is important spatial-explicitly information in urban geography research and urban planning or management; however, its extraction of fine-scale spatial information is still a difficult question in geographic information science (GIS). To overcome the difficulty in detecting intra-urban land use types using remotely sensed data alone and the positioning errors of mismatch in overlaying multi-temporal images, we have developed a digital reconstructing method to classify and detect intra-urban land use change through combining the “hierarchical classification” and “object-oriented segmentation” methods. This paper traces back to the historical developing process of Changchun city based on the above methods to classify and detect the detail intra-urban land use types including residential land, commercial land, industrial land, roads, water body and urban green space. The study has resolved the key problems on accurate spatial positioning from multi-scale and different data sources, classifying intra-urban land from function characteristics and detecting historical developing process of metropolis. Furthermore, we combined the SPOT5 imagery, 1:10000 topographic maps, historical maps, urban planning map and other auxiliary data of Changchun City to classify and de-tect its intra-urban land use change from 1905 to 2003. The results indicate that the methods are significant and effective in clas-sifying and detecting urban land use spatial information through combining the human-computer interactive interpretation with the expert-based knowledge from different source data. The methods can not only enhance the accuracy of urban land use classi-fication, but also improve spatial information extraction efficiency and positioning accuracy of multi-temporal spatial overlaying analysis.