A New Road Extraction Method from Satellite Images Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Liang:2024:GNC,
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author = "Jing Liang and Yaxin Chang and Ying Bi and
Caitong Yue and Boyang Qu and Mengnan Liu",
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title = "A New Road Extraction Method from Satellite Images
Using Genetic Programming",
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journal = "Guidance, Navigation and Control",
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note = "Accepted",
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keywords = "genetic algorithms, genetic programming, ANN, Road
extraction, feature construction",
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organisation = "CSAA, Technical Committee on Guidance, Navigation and
Control, Chinese Society of Aeronautics and
Astronautics",
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publisher = "World Scientific Publishing Co",
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ISSN = "2737-4807",
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URL = "https://www.worldscientific.com/doi/pdf/10.1142/S2737480724500092",
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DOI = "doi:10.1142/S2737480724500092",
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size = "31 pages",
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abstract = "Extracting roads from satellite images is an important
task in the field of computer vision with a wide range
of applications. However, efficient road extraction
from satellite images remains a complex challenge due
to issues such as data labeling and the diversity of
road features. Existing methods often struggle to
balance accuracy, robustness, and interpretability.
Genetic programming (GP) is based on a flexible and
interpretable structure that is robust and does not
require a large amount of data support. We position the
road extraction problem as a binary semantic
segmentation task and introduce GP algorithms. First,
an approach for extracting pixel neighborhood features
is proposed, and features from multiple images in the
DeepGlobe road extraction dataset are extracted. Then,
an advanced feature construction method based on GP is
employed. Finally, these advanced features are used for
training classifier and classification to achieve road
extraction. We have validated the effectiveness of the
approach on the Deep-Globe road extraction dataset. The
results demonstrated that the proposed approach
exhibits superior performance compared to traditional
classification methods and multilayer perceptron (MLP)
in terms of accuracy, generalization, and
interpretability. This study provides a valuable
reference for the integration of GP into the domain of
road extraction from satellite images, showcasing their
potential to enhance the accuracy and efficiency.",
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notes = "School of Electrical and Information Engineering,
Zhengzhou University, Zhengzhou, China",
- }
Genetic Programming entries for
Jing Liang
Yaxin Chang
Ying Bi
Caitong Yue
Boyang Qu
Mengnan Liu
Citations