Very High Resolution (VHR) imagery is becoming indispensable in urban mapping and land management. But owing to its characteristics, traditional spectral-based building extraction methods are not appropriate. This thesis presents an automated spectral-spatial integration extraction approach to extract two-dimensional roof outlines of urban buildings from IKONOS imagery. It substantially improves accuracy, compared with the well-known Extraction and Classification for Homogenous Objects (ECHO) and Iterative Self-Organizing Data Analysis Technique (ISODATA). With spectral-based Multi-peak Supervised Segmentation (MSS), the approach generates first stage classification. In the second stage, the original colour image and the first stage result are fed into a spatial structure classifier. With analysis of overlapped neighborhood's degree of membership to classes, homogeneity and cross-neighborhood structure, the first stage classification is significantly improved. Finally, buildings are extracted with post-processing. Compared with ISODATA, Signature Editor and ECHO, unique contributions are three new algorithms: MSS, Overlapped Neighborhood Analysis, and Cross-neighborhood Structure Detection.