Urban Road Network Extraction from Spaceborne SAR Image

A two-step method is developed for the extraction of road network from spaceborne SAR image: road candidates detection and connection. In the road candidates detection, classification of fused infrared and microwave SAR images effectively reduces the noise in the edge detection, and also well removes possible confusion of no-road objects, i.e. linearly-featured rivers in the edge image. Possible road candidates are further processed using the morphological thinning algorithm. Road candidates connection is carried out hierarchically according to road models we established. Finally, the main road network is established from the SAR image successfully. As an example, using the ERS-2 SAR image data, automatic detection of main road network in Shanghai Pudong area is presented. Introduction Urban road network extraction from spaceborne SAR image has been one of most important applications in remote sensing technology. For example, it is greatly helpful of urban transportation mapping, planning and management and city GIS database etc. During recent two decades, some approaches for automatic or semiautomatic detection of road from the optic or radar images have been developed [1-12]. However, due to some difficulties such as roads irregularity, multiplicative speckles, and complicated distribution of various objects in the urban area, these approaches do not seem to be well tractable to process the radar image, especially for distinguishing linearly featured objects, e.g. water body and roads. In this paper, a constant false alarm rate (CFAR) edge detector is first applied to extracting the potential roads as candidates. Then, classification of fused infrared and microwave SAR images is used to reduce the noise of the edge detection with logic “AND” fusion operation. Meanwhile, confusion of no-road objects such as linearly-featured rivers is removed. Further, the morphological thinning algorithm is employed to make the width of road candidates to be one pixel. To reduce complexity and time consuming, all roads candidates are classified into groups based on their orientations. To avoid possible loss of some useful road segments, the road candidates are linked and extended based on the thinning results and reference of original SAR images. As an example, using the ERS-2 SAR image data, automatic detection of main road network in Shanghai Pudong area is presented. Extraction of the Road Candidates from a SAR Image Three steps are applied to extraction of road candidates from a SAR image: edge detection, speckle reduction and edge thinning. The coefficient of variation detector based on the speckle model and statistics with CFAR has been employed to radar image edge detection [4]. Its threshold evaluating the homogeneity of the image is chosen as [13]. To reduce the confusion of no-road objects, such as grass, flat field and water body in the edge image, the logic “AND” fusion operation is carried out between it and the classification result of fused infrared and SAR images [14]. If one pixel as the road candidate in the edge image is classified as grass, or flat field, or water in the classification image, it should be re-assigned as the background instead of road. And the final road candidates are obtained by thinning the edges with morphological thinning algorithm. Linking Road Candidates After road candidates clustering based on their orientations, road linking and extension carried out hierarchically according to the characteristics of the main roads as well as their connections. To realize road candidates linking of the similar orientation, a model based on the characteristics of the single road in dense urban area and the connections between different roads is established as follows: 360 Progress In Electromagnetics Research Symposium 2005, Hangzhou, China, August 22-26 (1) The length and curvature of road candidates are within the given thresholds; (2) Connection takes place between two different road candidates; (3) The distance between road candidates satisfying (2) should be smaller than the given threshold; (4) The slope of the road formed with the endpoints satisfying (3) has little difference from the slopes of road candidates, which the two endpoints are belong to. Characteristic (1) indicates the local smoothness of the road, and (2) (3) (4) show the road continuity. As shown in Fig.1, only Point C is found to satisfy all demands, and the road candidate AC is added and the road candidate 3 is reserved. Endpoints of road candidates Road candidates and labels 1 Road candidates added Road candidates refused 4