V. Krylov, J. Nelson. "Fast road network extraction from remotely sensed images",
Advanced Concepts for Intelligent Vision Systems 2013,
Springer LNCS 8192, pp. 227-237, Poznan (Poland), October 28-31, 2013.
[link] [pdf] [presentation]
Abstract
This paper addresses the problem of fast, unsupervised road network extraction from remotely sensed images. We develop an approach that employs a fixed-grid, localized Radon transform to extract a redundant set of line segment candidates. The road network structure is then extracted by introducing interactions between neighbouring segments in addition to a data-fit term, based on the Bhattacharyya distance. The final configuration is obtained using simulated annealing via a Markov chain Monte Carlo iterative procedure. The experiments demonstrate a fast and accurate road network extraction on high resolution optical images of semi- urbanized zones, which is further supported by comparisons with several benchmark techniques.
Bibtex
@INPROCEEDINGS{KrylovACIVS13,
author={Krylov, VladimirA. and Nelson, James D.B.},
title={Fast road network extraction from remotely sensed images},
year={2013},
booktitle={Advanced Concepts for Intelligent Vision Systems},
volume={8192},
series={Lecture Notes in Computer Science},
editor={Jacques Blanc-Talon and Wilfried Philips and Dan Popescu and Paul Scheunders},
publisher={Springer},
pages={227--237}
}