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The automatic detection of road network from satellite and aerial images is highly significant in many actual applications, for instance, urban traffic measurement, military emergency response, and vehicle target tracking. Compared with other high-resolution satellite remote sensing images, high-resolution synthetic aperture radar (SAR) has become a popular research perspective for road detection owing to its insensitivity to the atmosphere and sun-illumination. However, the method of road network detection is still lagging due to the strong multiplicative speckle noise and complex background interference, causing the loss and break in the road segment extraction results. Aiming to solve this problem, a three-step road network detection framework is proposed. In the first step, the road segment candidates are extracted by the Fuzzy Local Information C-Means (FLICM) algorithm based on the gray-level co-occurrence matrix(GLCM) with Markov Random Fields (MRF), and it contains an adaptive parameter selection procedure which is presented for adjusting joint clustering parameters. In order to reduce false segments, we perform the local processing which combines the morphological operation, linearity index, and local Hough transform in the second step. Finally, as for the global road segment connection, we propose an improved region growing algorithm which fully considering the rationality of road elements to gain the road network. Compared with the traditional region growing algorithm, the proposed method can effectively promote the improvement of the integrity of the road network detection. Moreover, the performance of the proposed method is evaluated by comparing the results with the ground truth road map and the evaluation index including the completeness, correctness, and quality factor. In experiments, the algorithm has been verified with the SAR images from the different resolutions of the GF-3 satellite SAR image. The results of the various real images demonstrate that the proposed algorithm has improved considerably the adaptability and efficiency of road detection compared with other methods.
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