Work

A phase field model incorporating generic and specific prior knowledge applied to road network extraction from VHR satellite images

Year: 2007

Type: conference-paper

Abstract: We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach.

Source: British Machine Vision Conference (BMVC)

DOI: 10.5244/c.21.89

Authors: T. Peng, I. H. Jermyn, V. Prinet, J. Zerubia, B. Hu

Institutions: Institut national de recherche en sciences et technologies du numérique, Chinese Academy of Sciences, Institute of Automation

Language: English

Topic: Automated Road and Building Extraction

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

Funder: European Commission