Detection of linear features in SAR images: application to road network extraction
- 1 March 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 36 (2) , 434-453
- https://doi.org/10.1109/36.662728
Abstract
The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. The authors present two local line detectors as well as a method for fusing information from these detectors. In the second global step, they identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.Keywords
This publication has 31 references indexed in Scilit:
- Delineating buildings by grouping lines with MRFsIEEE Transactions on Image Processing, 1996
- A Bayesian multiple-hypothesis approach to edge grouping and contour segmentationInternational Journal of Computer Vision, 1993
- Potentials, valleys, and dynamic global coveringsInternational Journal of Computer Vision, 1990
- A Markovian Random field of piecewise straight linesBiological Cybernetics, 1989
- Snakes: Active contour modelsInternational Journal of Computer Vision, 1988
- A Computational Approach to Edge DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1986
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration techniqueComputer Graphics and Image Processing, 1981
- Thinning algorithms on rectangular, hexagonal, and triangular arraysCommunications of the ACM, 1972
- Use of the Hough transformation to detect lines and curves in picturesCommunications of the ACM, 1972