On detecting road regions in a single UAV image

Zhou, Hailing, Kong, Hui, Wei, Lei, Creighton, Douglas and Nahavandi, Saeid 2016, On detecting road regions in a single UAV image, IEEE transactions on intelligent transportation systems, In press, pp. 1-10, doi: 10.1109/TITS.2016.2622280.

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Title On detecting road regions in a single UAV image
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Kong, Hui
Wei, LeiORCID iD for Wei, Lei orcid.org/0000-0001-8267-0283
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name IEEE transactions on intelligent transportation systems
Season In press
Start page 1
End page 10
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-12-07
ISSN 1524-9050
Keyword(s) road detection
remote sensing
unmanned aerial vehicles (UAV)
aerial images
stroke width transform (SWT)
Summary Automatic detection of road regions in aerial images remains a challenging research topic. Most existing approaches work well on the requirement of users to provide some seedlike points/strokes in the road area as the initial location of road regions, or detecting particular roads such as well-paved roads or straight roads. This paper presents a fully automatic approach that can detect generic roads from a single unmanned aerial vehicles (UAV) image. The proposed method consists of two major components: automatic generation of road/nonroad seeds and seeded segmentation of road areas. To know where roads probably are (i.e., road seeds), a distinct road feature is proposed based on the stroke width transformation (SWT) of road image. To the best of our knowledge, it is the first time to introduce SWT as road features, which show the effectiveness on capturing road areas in images in our experiments. Different road features, including the SWT-based geometry information, colors, and width, are then combined to classify road candidates. Based on the candidates, a Gaussian mixture model is built to produce road seeds and background seeds. Finally, starting from these road and background seeds, a convex active contour model segmentation is proposed to extract whole road regions. Experimental results on varieties of UAV images demonstrate the effectiveness of the proposed method. Comparison with existing techniques shows the robustness and accuracy of our method to different roads.
Language eng
DOI 10.1109/TITS.2016.2622280
Field of Research 0905 Civil Engineering
1507 Transportation And Freight Services
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092615

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