Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Machine Learning Approach for Road-Line Extraction in Complex Urban Environments from High-Resolution Hyperspectral Image

Authors
Amol D. Vibhute1, *, Karbhari V. Kale2, Sandeep V. Gaikwad3, Arjun V. Mane4
1Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, 411016, MH, India
2Computer Science and IT, Dr. Babasaheb Ambedkar Technological University, Lonere, 402103, MH, India
3Department of Computer Applications, Charotar University of Science and Technology, Changa, 388421, Gujarat, India
4Department of Digital and Cyber Forensic, Government Institute of Forensic Science, Nagpur, MH, India
*Corresponding author. Email: amolvibhute2011@gmail.com
Corresponding Author
Amol D. Vibhute
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_39How to use a DOI?
Keywords
Road-line extraction; Machine learning; Hyperspectral image; Mathematical morphology; Support vectors
Abstract

Road network extraction and road line extraction from remote sensing images is still challenging task due to the complex structure of urban areas. The spectral response, design, shape, size, shadow, a contrast of roads, and other urban features are similar, which causes inaccurate results. The present paper investigates the asphalt road line extraction from high spatial-spectral resolution hyperspectral imagery. The implemented approach is based on a machine-learning algorithm, i.e., Support Vector Machines (SVM), structural information, and road line filtering. Road and non-road classification have been done using the SVM algorithm, generating a road map. In the second step, mathematical morphology was used to extract the road network with enhanced precision. Unwanted material has been removed using the granulometry approach. Finally, accurate and comprehensive road line extraction has been done by median filtering. The results have shown 85.13% correctness with 79.93% completeness of the implemented methodology. The experimental results are helpful for transportation analysis, traffic management, cartography, urban planning, and its management.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
978-94-6463-196-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_39How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Amol D. Vibhute
AU  - Karbhari V. Kale
AU  - Sandeep V. Gaikwad
AU  - Arjun V. Mane
PY  - 2023
DA  - 2023/08/10
TI  - Machine Learning Approach for Road-Line Extraction in Complex Urban Environments from High-Resolution Hyperspectral Image
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 511
EP  - 520
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_39
DO  - 10.2991/978-94-6463-196-8_39
ID  - Vibhute2023
ER  -