Road Network Extraction from High-Resolution SAR Imagery Based on the Network Snake Model

Automatic road network extraction from satellite images is currently considered to be an important research trend in the field of remote sensing and photogrammetry. This paper presents a method for automatic extraction of road networks from synthetic aperture radar (SAR) imagery. The method consists of three steps. During the first step, road area candidates are detected based on the fusion of extracted features: minimum radiance, contrast, and direction of minimum radiance. In the second step, several refinement criteria are used in order to discard detected false candidates, and a thinning morphology operator is applied on the road areas to extract the road segments. Seed points of interest are then extracted to using in a network snake model, which is employed in the third step to connect the seed points in order to form the road network. Minimizing the energy function of the Snake model and then using a perceptual grouping algorithm is for discarding redundant segments and avoiding gaps between segments is used sequentially in the network snake model. The proposed algorithm is tested on TerraSAR-X images with different areas. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality. An accuracy assessment showed that the proposed model is capable of achieving a quality index between 56 to 82.5 percent.