Identification of Roads from RS Images

Authors(2) :-P. Bala, Dr. K. Suvarna Vani

As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing (RS) image has been a hot research topic in recent years. In the project, after analysing different road features and road models, the road extraction methods were classified into the classification-based methods, knowledge-based methods, mathematical morphology, active contour model, and dynamic programming. Firstly, the road features, road model, existing difficulties and interference factors for road extraction were analysed. Secondly, the principle of road extraction, the advantages and disadvantages of various methods and research achievements were briefly highlighted. Then, the comparisons of the different road extraction algorithms were performed, including road features, test samples and shortcomings. Finally, the research results in recent years were summarized emphatically. It is obvious that only using one kind of road features is hard to get an excellent extraction effect.

Authors and Affiliations

P. Bala
Computer Science Department, V.R.Siddhartha Engineering College, Student, Vijayawada, India
Dr. K. Suvarna Vani
Computer Science Department, V.R.Siddhartha Engineering College, Professor, Vijayawada, India

Remote Sensing Image, GPS navigation, Traffic management, City Planning, Road Extraction Methods.

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 356-360
Manuscript Number : CSEIT172575
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

P. Bala, Dr. K. Suvarna Vani, "Identification of Roads from RS Images", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.356-360, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT172575

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