Next Article in Journal
A New Method for Node Fault Detection in Wireless Sensor Networks
Next Article in Special Issue
Simulation of EO-1 Hyperion Data from ALI Multispectral Data Based on the Spectral Reconstruction Approach
Previous Article in Journal
Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels
Previous Article in Special Issue
Detection of Seagrass Distribution Changes from 1991 to 2006 in Xincun Bay, Hainan, with Satellite Remote Sensing
Sensors 2009, 9(2), 1237-1258; doi:10.3390/s90201237
Article

Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation

1,2
, 1,* , 1
 and 2
Received: 31 October 2008; in revised form: 20 February 2009 / Accepted: 24 February 2009 / Published: 24 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
View Full-Text   |   Download PDF [843 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extractroad features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads’ geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms.
Keywords: Semi-automatic; road tracking; profile matching; template matching; angular texture signature; parallelepiped classification; lane marking Semi-automatic; road tracking; profile matching; template matching; angular texture signature; parallelepiped classification; lane marking
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Lin, X.; Liu, Z.; Zhang, J.; Shen, J. Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation. Sensors 2009, 9, 1237-1258.

AMA Style

Lin X, Liu Z, Zhang J, Shen J. Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation. Sensors. 2009; 9(2):1237-1258.

Chicago/Turabian Style

Lin, Xiangguo; Liu, Zhengjun; Zhang, Jixian; Shen, Jing. 2009. "Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation." Sensors 9, no. 2: 1237-1258.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert