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Nady, B., Mostafa, Y., Abbas, Y., Enieb, M. (2020). USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT. JES. Journal of Engineering Sciences, 48(No 1), 20-31. doi: 10.21608/jesaun.2020.109051
Beshoy Nady; Yasser Mostafa; Yousef A. Abbas; Mahmoud Enieb. "USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT". JES. Journal of Engineering Sciences, 48, No 1, 2020, 20-31. doi: 10.21608/jesaun.2020.109051
Nady, B., Mostafa, Y., Abbas, Y., Enieb, M. (2020). 'USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT', JES. Journal of Engineering Sciences, 48(No 1), pp. 20-31. doi: 10.21608/jesaun.2020.109051
Nady, B., Mostafa, Y., Abbas, Y., Enieb, M. USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT. JES. Journal of Engineering Sciences, 2020; 48(No 1): 20-31. doi: 10.21608/jesaun.2020.109051

USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT

Article 3, Volume 48, No 1, January and February 2020, Page 20-31  XML PDF (942.26 K)
Document Type: Research Paper
DOI: 10.21608/jesaun.2020.109051
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Authors
Beshoy Nady* 1; Yasser Mostafa2; Yousef A. Abbas1; Mahmoud Enieborcid 1
1Civil Eng. Dept., Faculty of Engineering, Assuit University, Assuit, Egypt
2Civil Eng. Dept., Faculty of Engineering, Sohag University, Sohag, Egypt
Abstract
Roads extraction from VHR satellite images are very paramount for GIS and map updating. Due to the high resolution of satellite images, there are many obstacles broken roads such as shadow, and vehicles. The present work aims to find the most suitable road extraction approach that can be applied in the Egyptian environment. In this study, two satellite images from WorldView-2 and WorldView-3 were used. Classification of image by pixel-based and object-based was carried out to find the appropriate classification method for road extraction. Then, road class refinement by morphology and angular texture signature are performed to decrease the misclassifications between roads and other spectrally similar objects. After that, an iterative and localized Hough transform method was compared with the thinning algorithm method to find the proper method that can extract road centerline segments from the refined images. The performance of the extracted roads was estimated by using the common metrics; completeness, correctness, and quality. The results of this work demonstrate that the random tree in object-based classification achieves the highest overall accuracy than other classification methods. Also, thinning algorithm has more advantages than Hough transform.
Keywords
Road network; Hough transform; thinning algorithm; morphology; road centerline extraction; and classification
Main Subjects
Civil Engineering: structural, Geotechnical, reinforced concrete and steel structures, Surveying, Road and traffic engineering, water resources, Irrigation structures, Environmental and sanitary engineering, Hydraulic, Railway, construction Management.
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