Automated road pavement marking detection from high resolution aerial images based on multi-resolution image analysis and anisotropic Gaussian filtering
Road features extraction from remotely 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.
road pavement marking fetzture extraction high resolution aerial image multi-resohuion image analysis anosotropic Gaussian filtering
Hang Jin Yanming Feng
Faculty of Science and Technology Queensland University of Technology Brisbane, Australia
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
337-341
2010-07-05（万方平台首次上网日期，不代表论文的发表时间）