Road Extraction Using an Improved Snake Model and CART
- Yi-Nan Lu, Zhe Zhang, Xiao-Ni Liu, Yun-Fan Du
- Corresponding Author
- Yi-Nan Lu
Available Online November 2016.
- https://doi.org/10.2991/ceis-16.2016.75How to use a DOI?
- road extraction; GVF-Snake; classification; CART
- 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.
- 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 SN - 2352-538X UR - https://doi.org/10.2991/ceis-16.2016.75 DO - https://doi.org/10.2991/ceis-16.2016.75 ID - Lu2016/11 ER -