Fast Road Network Extraction from Remotely Sensed Images
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.
KeywordsRoad network remote sensing localized Radon transform Markov chain Monte Carlo
Unable to display preview. Download preview PDF.
- 3.Chai, D., Forstner, W., Lafarge, F.: Recovering line-networks in images by junction-point processes. In: Proc. of IEEE Conf. Computer Vision and Pattern Recognition, Portland, US (2013)Google Scholar
- 12.Negri, M., Gamba, P., Lisini, G., Tupin, F.: Junction-aware extraction and regularization of urban road networks in high-resolution sar images. IEEE Trans. Geosci. Remote Sens., 2962–2971 (2006)Google Scholar