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Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.