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Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

By Hang Jin and Yanming Feng

Abstract

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach

Topics: 090901 Cartography, 090903 Geospatial Information Systems, 090905 Photogrammetry and Remote Sensing, road pavement marking, feature extraction, high resolution aerial image, multi-resolution image analysis, anosotropic Gaussian filtering
Publisher: IEEE Computer Society
Year: 2010
DOI identifier: 10.1109/ICSPS.2010.5555636
OAI identifier: oai:eprints.qut.edu.au:31172

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