Abstract

Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. The extracted feature vector is then matched with some template vectors in order to find the best matched intersection pattern, obtain the type of intersection and the direction of connected roads. The experimental analysis are carried out with ISPRS Vaihingen and Toronto images. The experimental results show that the proposed method can extract most of the road intersections correctly. For the Vaihingen image, the the completeness and correctness are 81% and 87%, respectfully, while for the Toronto image, the the completeness and correctness are 78% and 85%, respectfully. It can help to build more correct and complete road network.

Details

Title
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY
Author
P Li 1 ; Y Li 2 ; Feng, J 3 ; Z Ma 1 ; X Li 1 

 Chongqing Geomatics and Remote Sensing Center, 404100 Chongqing, China; Chongqing Geomatics and Remote Sensing Center, 404100 Chongqing, China 
 School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China; School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China 
 School of Remote Sensing and Information Engineering Engineering, Wuhan Uni versity, Wuhan,430079, China; School of Remote Sensing and Information Engineering Engineering, Wuhan Uni versity, Wuhan,430079, China 
Pages
113-117
Publication year
2020
Publication date
2020
Publisher
Copernicus GmbH
ISSN
16821750
e-ISSN
21949034
Source type
Conference Paper
Language of publication
English
ProQuest document ID
2435875419
Copyright
© 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.