Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines

Abstract

This paper investigates road centreline extraction from high‐resolution imagery. A novel road detection system is proposed based on multiscale structural features and support vector machines (SVMs). The salient aspects of the strategy are: (1) structural features are exploited because road objects are narrow and extensive, with large perimeters and small radii; (2) the object‐based approach is used to extract multiscale information so as to reduce the local spectral variation caused by vehicles, shadows, road markings, etc.; (3) the hybrid spectral–structural features are analysed using the SVM classifier; and (4) multiple object levels are integrated because a multiscale approach can exploit the rich spatial information and detect multiscale road objects. Experiments were conducted on two IKONOS multispectral datasets and the results validated the proposed method.