Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

Road Extraction Using an Improved Snake Model and CART

Authors
Yi-Nan Lu, Zhe Zhang, Xiao-Ni Liu, Yun-Fan Du
Corresponding Author
Yi-Nan Lu
Available Online November 2016.
DOI
https://doi.org/10.2991/ceis-16.2016.75How to use a DOI?
Keywords
road extraction; GVF-Snake; classification; CART
Abstract
Road Extraction from remote sensing images has been an important research topic. It is difficult to extract the road quickly and reliably due to the complexity of the road features. In this paper, an improved GVF-Snake algorithm as a segmentation method automatically labels training samples to reduce the complexity of the manual labeling data, and a Classification and Regression Tree method is used to extract the roads from remote sensing images by classification. The experiments indicate that the proposed method can efficiently and automatically extract the roads from remote sensing images.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering and Information Systems
Part of series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-283-1
DOI
https://doi.org/10.2991/ceis-16.2016.75How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yi-Nan Lu
AU  - Zhe Zhang
AU  - Xiao-Ni Liu
AU  - Yun-Fan Du
PY  - 2016/11
DA  - 2016/11
TI  - Road Extraction Using an Improved Snake Model and CART
BT  - 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
UR  - https://doi.org/10.2991/ceis-16.2016.75
DO  - https://doi.org/10.2991/ceis-16.2016.75
ID  - Lu2016/11
ER  -