Map-based road detection in spaceborne synthetic aperture radar images based on curvilinear structure extraction

This paper presents an automatic map-based road detection algorithm for spaceborne synthetic aperture radar (SAR) images. Our goal is to find roads in a SAR image with subpixel accuracy with the help of a digital map. There are location errors between the digital map and the geocoded SAR image, whic...

Full description

Saved in:
Bibliographic Details
Published in:Optical Engineering 2000-09, Vol.39 (9), p.2413-2421
Main Authors: Jeon, Byoung-Ki, Jang, Jeong-Hun, Hong, Ki-Sang
Format: Article
Language:English
Subjects:
active contour model (snake)
curvilinear structure
digital map
generalized Hough transform (gHT)
potential field
road detection
spaceborne synthetic aperture radar (SAR) image
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an automatic map-based road detection algorithm for spaceborne synthetic aperture radar (SAR) images. Our goal is to find roads in a SAR image with subpixel accuracy with the help of a digital map. There are location errors between the digital map and the geocoded SAR image, which are about 20 to 30 pixels, and we adopt a coarse-to-fine strategy to reduce it. In the coarse matching step, we roughly find the locations of roads by a simple search using water areas or a generalized Hough transform based on digital map information. The fine matching step detects roads accurately by using the active contour model (snake). The input of the snake operation is the potential field constructed from the extracted ridges or ravines of curvilinear structures in the SAR image. Experimental results show that our algorithm detects roads with average error of less than one pixel. ©
ISSN:0091-3286
1560-2303
DOI:10.1117/1.1287399