Junction‐aware water flow approach for urban road network extraction
Open Access
- 1 March 2016
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Image Processing
- Vol. 10 (3), 227-234
- https://doi.org/10.1049/iet-ipr.2015.0263
Abstract
The highways now offer more and more complex road junctions composed of many surrounding roads, overlapping each other with high curvature. Traditional road detection methods are not adequate for the rapid development of cities with increased complexity of the road junction shape. The major challenges in road extraction are varying spectral reflectance, lane markings, obstacles with different sizes, of various shapes, and intersected roads. However, very few researchers have attempted handling overlapped roads with low curvature only. In this study, a water flow-based semi-automatic approach is proposed for extracting road network with various shapes of junctions (Y-shaped with different acute angles), intersected and also for overlapped high curvilinear roads. Recognising the complex road junction is done with fewer automatically generated anchor points without human intervention, which detects the number of roads (branches) connected to that junction along the road's width, orientation and length with less computation time. Hence, from a manually selected seed point, the authors' algorithm can be automatically propagated throughout a whole road network with or without single lane or multiple lanes (lined, dotted or both). Experimental results show that this proposed approach can accurately and efficiently extract interconnected road network from QuickBird images with minimal seed points.Keywords
This publication has 13 references indexed in Scilit:
- Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation TechniquesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
- Road Network Detection Using Probabilistic and Graph Theoretical MethodsIEEE Transactions on Geoscience and Remote Sensing, 2012
- Automatic Road Extraction from High-Resolution Remote Sensing Image Based on Bat Model and Mutual Information MatchingJournal of Computers, 2011
- Semi-automatic extraction of road networks by least squares interlaced template matching in urban areasInternational Journal of Remote Sensing, 2011
- Unsupervised line network extraction in remote sensing using a polyline processPattern Recognition, 2010
- Extracting Urban Road Networks from High-resolution True Orthoimage and LidarPhotogrammetric Engineering & Remote Sensing, 2008
- Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road FootprintsIEEE Transactions on Geoscience and Remote Sensing, 2007
- A Gibbs Point Process for Road Extraction from Remotely Sensed ImagesInternational Journal of Computer Vision, 2004
- State of the art on automatic road extraction for GIS update: a novel classificationPattern Recognition Letters, 2003
- Detection of line junctions and line terminations using curvilinear featuresPattern Recognition Letters, 2000