Urban areas are a complex combination of various land-cover types, and show a variety of land-use structures and spatial layouts. Furthermore, the spectral similarity between built-up areas and bare land is a great challenge when using high spatial resolution remote sensing images to map urban areas, especially for images obtained in dry and cold seasons or high-latitude regions. In this study, a new procedure for urban area extraction is presented based on the high-level, regional, and line segment features of high spatial resolution satellite data. The urban morphology is also analyzed. Firstly, the primitive features—the morphological building index (MBI), the normalized difference vegetation index (NDVI), and line segments—are extracted from the original images. Chessboard segmentation is then used to segment the image into the same-size objects. In each object, advanced features are then extracted based on the MBI, the NDVI, and the line segments. Subsequently, object-oriented classification is implemented using the above features to distinguish urban areas from non-urban areas. In general, the boundaries of urban and non-urban areas are not very clear, and each urban area has its own spatial structure characteristic. Hence, in this study, an analysis of the urban morphology is carried out to obtain a clear regional structure, showing the main city, the surrounding new development zones, etc. The experimental results obtained with six WorldView-2 and Gaofen-2 images obtained from different regions and seasons demonstrate that the proposed method outperforms the current state-of-the-art methods.