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Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model

El Ghoul, A.; Jermyn, I.H.; Zerubia, J.

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Authors

A. El Ghoul

J. Zerubia



Contributors

N. Paparoditis
Editor

M. PierrotDeseilligny
Editor

E. Mallet
Editor

O. Tournaire
Editor

Abstract

We propose a new algorithm for network segmentation from very high resolution (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.

Citation

El Ghoul, A., Jermyn, I., & Zerubia, J. (2010). Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. In N. Paparoditis, M. PierrotDeseilligny, E. Mallet, & O. Tournaire (Eds.), PCV 2010: Photogrammetric computer vision and image analysis part 1 (215-220)

Conference Name ISPRS-Technical-Commission III Symposium on Photogrammetric Computer Vision and Image Analysis (PCV)
Conference Location Saint Mande
Publication Date Sep 1, 2010
Deposit Date Aug 12, 2011
Publicly Available Date Mar 28, 2024
Volume 38
Pages 215-220
Series Number 3A
Series ISSN 2194-9034
Book Title PCV 2010: Photogrammetric computer vision and image analysis part 1.
Public URL https://durham-repository.worktribe.com/output/1159175
Publisher URL http://www.isprs.org/proceedings/XXXVIII/part3/

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