Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91288
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Title: Road extraction from high resolution remote sensing images based on vector field learning
Authors: Liang, P
Shi, W:rp00069 
Ding, Y
Liu, Z
Shang, H
Issue Date: May-2021
Source: Sensors, May 2021, v. 21, no. 9, 3152
Abstract: Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.
Keywords: DCNN
Encoder-decoder
High resolution remote sensing image
Road extraction
Vector field learning
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors 
EISSN: 1424-8220
DOI: 10.3390/s21093152
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Liang, P.; Shi,W.; Ding, Y.;Liu, Z.; Shang, H. Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning. Sensors 2021, 21, 3152 is available at https://doi.org/10.3390/s21093152
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