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Publications about Road network extraction
Result of the query in the list of publications :
3 Articles |
1 - Extended Phase Field Higher-Order Active Contour Models for Networks.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, May 2010.
Keywords : Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction.
@ARTICLE{Peng09,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{ Extended Phase Field Higher-Order Active Contour Models for Networks}, |
| year |
= |
{2010}, |
| month |
= |
{May}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{88}, |
| number |
= |
{1}, |
| pages |
= |
{ 111-128}, |
| url |
= |
{http://www.springerlink.com/content/d3641g2227316w58/}, |
| keyword |
= |
{Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction} |
| } |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a ‘network’, i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present new phase field higher-order active contour (HOAC) prior models for network regions, and apply them to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much ‘noise’ in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular. |
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2 - Unsupervised line network extraction in remote sensing using a polyline process.
C. Lacoste and X. Descombes and J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, April 2010.
Keywords : Marked point process, Line networks, Road network extraction.
@ARTICLE{lacoste10,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Unsupervised line network extraction in remote sensing using a polyline process}, |
| year |
= |
{2010}, |
| month |
= |
{April}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{43}, |
| number |
= |
{4}, |
| pages |
= |
{1631-1641}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2009.11.003}, |
| keyword |
= |
{Marked point process, Line networks, Road network extraction} |
| } |
Abstract :
Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one. |
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3 - A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images.
P. Cariou and X. Descombes and E. Zhizhina. Problems of Information Transmission, 10(3): pages 247-256, 2010.
Keywords : Marked point process, birth and death process, Road network extraction.
@ARTICLE{cariou2010,
|
| author |
= |
{Cariou, P. and Descombes, X. and Zhizhina, E.}, |
| title |
= |
{A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images}, |
| year |
= |
{2010}, |
| journal |
= |
{Problems of Information Transmission}, |
| volume |
= |
{10}, |
| number |
= |
{3}, |
| pages |
= |
{247-256}, |
| url |
= |
{ http://www.jip.ru/2010/247-256-2010.pdf}, |
| keyword |
= |
{Marked point process, birth and death process, Road network extraction} |
| } |
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Conference article |
1 - Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model.
A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010.
Keywords : Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing.
Copyright : ISPRS
@INPROCEEDINGS{Elghoul10a,
|
| author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{https://hal.inria.fr/inria-00491017}, |
| keyword |
= |
{Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing} |
| } |
Abstract :
We propose a new algorithm for network segmentation from VHR remote sensing images. The algorithm performs this task quasi-automatically,
that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not
come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images. |
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