On Detecting Road Regions in a Single UAV Image

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 7; pp. 1713 - 1722
• Main Authors: Hailing Zhou, Hui Kong, Lei Wei, Creighton, Douglas, Nahavandi, Saeid
• Format: Journal Article
• Language: English
• Published: IEEE 01.07.2017
• Subjects: aerial images; Geometry; Image color analysis; Image edge detection; Image segmentation; remote sensing; Road detection; Roads; Satellites; segmentation; stroke width transform (SWT); Unmanned aerial vehicles; unmanned aerial vehicles (UAV)
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2622280

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.