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

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

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, September). Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. Presented at ISPRS-Technical-Commission III Symposium on Photogrammetric Computer Vision and Image Analysis (PCV), Saint Mande

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ISPRS-Technical-Commission III Symposium on Photogrammetric Computer Vision and Image Analysis (PCV)
• Publication Date: Sep 1, 2010
• Deposit Date: Aug 12, 2011
• Publicly Available Date: Apr 14, 2016
• 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/