A Method for Accurate Road Centerline Extraction From a Classified Image

Abstract

Accurate road centerline extraction plays an important role in practical remote sensing applications. Most existing centerline extraction methods have many limitations when the classified image contains complicated objects such as curvilinear, close, or short extent features. To cope with these limitations, this study presents a novel accurate centerline extraction method that integrates tensor voting, principal curves, and the geodesic method. The proposed method consists of three main steps. Tensor voting is first used to extract feature points from the classified image. The extracted feature points are then projected onto the principal curves. Finally, the feature points are linked by the geodesic method to create the central line. The experimental results demonstrate that the proposed method, which is automatic, provides a comparatively accurate solution for centerline extraction from a classified image.