Fully Automated Road Network Extraction from High-Resolution Satellite Multispectral Imagery

Abstract

In this paper we present a fully automated technique for road network extraction from high-resolution multispectral satellite imagery of urban areas. Road segments are iteratively identified by examining contextual length-width features extracted from the multispectral imagery in conjunction with a vegetation index. As straight-line road segments are identified, the endpoints of these line segments are grown, allowing the road network extraction algorithm to track roads around curves and through areas that are partially occluded. Long line segments are iteratively added to the road network, and a buffer is set up around them to exclude any line segments that are not close to perpendicular to the identified road network segments. This algorithm is fully automated and requires no interaction with the user after initial setting of several parameters controlling the identification of potential road pixels, growth of the line segments, and the stopping criteria. The proposed approach yields an accurate road network with minimal interaction from the user. Extraction completeness measures of 82-85% and correctness measures of 71-84% are obtained.