PNNL

Abstract

Road networks represent a vital component of geospatial data sets in high demand, and thus contribute significantly to extraction labor costs. Multispectral imagery has only recently become widely available at high spatial resolutions, and modeling spectral content has received limited consideration for road extraction algorithms. This paper presents a methodology that exploits spectral content for fully automated road centerline extraction. Preliminary detection of road centerline pixel candidates is performed with Anti-parallel-edge Centerline Extraction (ACE). This is followed by constructing a road vector topology with a fuzzy grouping model that links nodes from a self-organized mapping of the ACE pixels. Following topology construction, a self-supervised road classification (SSRC) feedback loop is implemented to automate the process of training sample selection and refinement for a road class, as well deriving practical spectral definitions for non-road classes. SSRC demonstrates a potential to provide dramatic improvement in road extraction results by exploiting spectral content. Road centerline extraction results are presented for three 1m color-infrared suburban scenes, which show significant improvement following SSRC.