TY - JOUR
T1 - A Direction-Guided Ant Colony Optimization Method for Extraction of Urban Road Information from Very-High-Resolution Images
AU - Yin, Dandong
AU - Du, Shihong
AU - Wang, Shaowen
AU - Guo, Zhou
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2015/10
Y1 - 2015/10
N2 - Typical object-based classification methods only take image object properties as criteria to classify roads, leaving the associated edge information unused. These methods often lead to fragmented road areas and inconsistent road widths and smoothness. Meanwhile, very-high-resolution (VHR) images contain a large amount of edge information and different types of geographic objects, thus, it is challenging to extract roads by typical edge-based extraction or grouping methods. In this study, a globally optimized method is developed to integrate both object and edge features to extract urban road information from VHR images. This novel method extends ant colony optimization (ACO) through deploying and moving ants (artificial agents) along roads with the guidance of comprehensive object and edge information. As ants spread pheromone along their paths, roads are recognized based on aggregated pheromone levels. A set of experiments on VHR images showed that our method significantly outperforms object-based classification methods with not only improved road extraction quality but also enhanced stability when applied to large and complex images.
AB - Typical object-based classification methods only take image object properties as criteria to classify roads, leaving the associated edge information unused. These methods often lead to fragmented road areas and inconsistent road widths and smoothness. Meanwhile, very-high-resolution (VHR) images contain a large amount of edge information and different types of geographic objects, thus, it is challenging to extract roads by typical edge-based extraction or grouping methods. In this study, a globally optimized method is developed to integrate both object and edge features to extract urban road information from VHR images. This novel method extends ant colony optimization (ACO) through deploying and moving ants (artificial agents) along roads with the guidance of comprehensive object and edge information. As ants spread pheromone along their paths, roads are recognized based on aggregated pheromone levels. A set of experiments on VHR images showed that our method significantly outperforms object-based classification methods with not only improved road extraction quality but also enhanced stability when applied to large and complex images.
KW - Ant colony optimization (ACO)
KW - image classification
KW - object-based image analysis (OBIA)
KW - road extraction
UR - http://www.scopus.com/inward/record.url?scp=84943369770&partnerID=8YFLogxK
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U2 - 10.1109/JSTARS.2015.2477097
DO - 10.1109/JSTARS.2015.2477097
M3 - Article
AN - SCOPUS:84943369770
SN - 1939-1404
VL - 8
SP - 4785
EP - 4794
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 10
M1 - 7293629
ER -