Road Extraction From High-Resolution Satellite Images Based on Multiple Descriptors

Abstract

Geometry and texture noise make it difficult to accurately describe road image rules, which leads to the low degree of automation of traditional template matching algorithms based on internal texture homogenization. We propose a semi-automatic road extraction method based on multiple descriptors to improve the degree of automation while ensuring the accuracy of road extraction. This method aims to address the problems of incomplete road image geometric information and poor homogeneity of internal road texture. The multiscale line segment orientation histogram model and sector descriptor are established. Road points are tracked by interpolation and extension, and postprocessing is used to fit the tracking points and extract the road routes. In this article, high-resolution remote sensing images of different types, different resolutions, and different scenes are selected, and the roads exhibit curvatures, vehicle and shadow occlusions, roundabouts, and variational features. Experiments show that for roads with a certain width, completeness, and correctness of the method are more than 98%. Additionally, as compared with other algorithms, the interactive human intervention of this method is reduced by more than 2/3.