• DocumentCode
    68354
  • Title

    A Semi-Automatic Method for Road Centerline Extraction From VHR Images

  • Author

    Zelang Miao ; Bin Wang ; Wenzhong Shi ; Hua Zhang

  • Author_Institution
    Dept. of Land Surveying & Geo-Inf., Hong Kong Polytech. Univ., Kowloon, China
  • Volume
    11
  • Issue
    11
  • fYear
    2014
  • fDate
    Nov. 2014
  • Firstpage
    1856
  • Lastpage
    1860
  • Abstract
    This letter presents a semi-automatic approach to delineating road networks from very high resolution satellite images. The proposed method consists of three main steps. First, the geodesic method is used to extract the initial road segments that link the road seed points prescribed in advance by users. Next, a road probability map is produced based on these coarse road segments and a further direct thresholding operation separates the image into two classes of surfaces: the road and nonroad classes. Using the road class image, a kernel density estimation map is generated, upon which the geodesic method is used once again to link the foregoing road seed points. Experiments demonstrate that this proposed method can extract smooth correct road centerlines.
  • Keywords
    differential geometry; geophysical image processing; image segmentation; probability; roads; VHR satellite imaging; geodesic method; initial road segment extraction; kernel density estimation map; road centerline extraction; road class imaging; road network delineating; road probability map; road seed points link; semiautomatic method; thresholding operation; very high resolution satellite imaging; Educational institutions; Estimation; Feature extraction; Kernel; Remote sensing; Roads; Satellites; Kernel density estimation (KDE); geodesic method; mean shift; road extraction; semi-automatic; very high resolution (VHR) satellite images;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2014.2312000
  • Filename
    6784347