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Proceedings Paper

A new generic method for semi-automatic extraction of river and road networks in low- and mid-resolution satellite images
Author(s): Jacopo Grazzini; Scott Dillard; Pierre Soille
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Paper Abstract

This paper addresses the problem of semi-automatic extraction of road or hydrographic networks in satellite images. For that purpose, we propose an approach combining concepts arising from mathematical morphology and hydrology. The method exploits both geometrical and topological characteristics of rivers/roads and their tributaries in order to reconstruct the complete networks. It assumes that the images satisfy the following two general assumptions, which are the minimum conditions for a road/river network to be identifiable and are usually verified in low- to mid-resolution satellite images: (i) visual constraint: most pixels composing the network have similar spectral signature that is distinguishable from most of the surrounding areas; (ii) geometric constraint: a line is a region that is relatively long and narrow, compared with other objects in the image. While this approach fully exploits local (roads/rivers are modeled as elongated regions with a smooth spectral signature in the image and a maximum width) and global (they are structured like a tree) characteristics of the networks, further directional information about the image structures is incorporated. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given network seed with this metric is combined with hydrological operators for overland flow simulation to extract the paths which contain most line evidence and identify them with the target network.

Paper Details

Date Published: 22 October 2010
PDF: 10 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783007 (22 October 2010); doi: 10.1117/12.865052
Show Author Affiliations
Jacopo Grazzini, Los Alamos National Lab. (United States)
Scott Dillard, Pacific Northwest National Lab. (United States)
Pierre Soille, European Commission Joint Research Ctr. (Italy)

Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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