Urban road extraction from high‐resolution optical satellite images
In recent years, many approaches have been exploited for automatic urban road extraction. Most of these approaches are based on edge and line detecting algorithms. In this paper, a new integrated system for automatic extraction of main roads in high‐resolution optical satellite images is present. Firstly, a multi‐scale greylevel morphological cleaning algorithm is proposed to reduce the grey deviation of the road regions. Secondly, based on the greylevel difference between road surfaces and environmental objects, a colour high‐resolution satellite image is segmented into a simplified imagemap by using the mean shift algorithm, which consists of three stages. The first stage deals with image filtering, the second stage deals with colour segmentation, and the third stage is proposed to fuse small regions in the segmented image. The mean shift filter algorithm not only smoothes the image, but also preserves abrupt changes (i.e. edges) in the local structure. The mean shift segmentation algorithm is a straightforward extension of the smoothing algorithm, which preserves discontinuity. From the histogram of the simplified imagemap, we can find the potential road surfaces, and use greylevel threshold to convert the segmented image into a binary one. The binary image is processed by using binary mathematical morphological closing and opening to remove small objects and to open the connected street blocks. We use a contour tracing algorithm to remove holes in street‐block regions and to detect the street blocks' contours. In this research we found that many street blocks' contours were preserved perfectly, except for some of them which were depressed. Finally, we utilize the convex hull algorithm to smooth the street blocks' zigzag edges and to close the gaps in some street blocks, and then, we get the road edges. The integrated system for road network extraction is tested on the red band of an IKONOS multispectral image; all algorithms in this study are developed in C++ under Windows XP operating system. Results of the road network extraction are presented to illustrate the validation of the extracting strategy and the corresponding algorithms in this research, and future prospects are exposed.
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Document Type: Research Article
Affiliations: Department of Image Processing, Institute of Remote Sensing Applications, Chinese Academy of Science, PO Box 9718, Beijing, 100101, China
Publication date: 20 November 2005