Abstract
It is very important to extract accurate road networks from high resolution remote sensing images for various applications, such as transportation database updating. However, existing approaches cannot get satisfactory results. We propose an improved road networks extraction from remote sensing images based on the shear transform, the directional segmentation, the road probability, shape features and a skeletonization algorithm. The proposed method includes the following steps. First, we combine shear transform with directional segmentation to get the initial road regions. Second, road map based on Mahalanobis distance and thresholding is fused with the initial road regions to improve accuracy. Third, road shape features filtering and hole filling are used to extract reliable road segments. Finally, the road centerlines are extracted by an automatic subvoxel precise skeletonization method based on fast marching. Road networks are then generated by post-processing. Experimental results show that the proposed method can extract smooth and correct road centerlines.
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