Automatic road extraction from remote sensing images based on a Hessian matrix

The road network is one of the most important types of information in the Geographic Information System (GIS). However, automatic extraction of roads is still considered a challenging problem. In this paper, we focus on robust extraction of main roads. In the proposed algorithm, we first determine the roadness of each pixel using the eigenvalues of its Hessian matrix. The roadness represents the belongingness of a pixel to a road; and its determination is performed on a multi-scale basis so that it is robust to various widths of roads. We then perform directional grouping to the determined initial road map and remove outliers in each group via directionally morphological filtering. Finally, we determine roads by combining the results from each group. Experimental results show that the proposed algorithm can automatically extract most main roads in various remote sensing images.