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Media type:
Text;
E-Article
Title:
Using building and bridge information for adapting roads to ALS data by means of network snakes
Contributor:
Goepfert, Jens
[Author];
Rottensteiner, Franz
[Author]
Published:
Göttingen : Copernicus GmbH, 2010
Published in:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [PCV 2010 - Photogrammetric Computer Vision And Image Analysis, Pt I] 38 (2010), Nr. Part 3A
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
In the German Authoritative Topographic Cartographic Information System (ATKIS), the 2D positions and the heights of objects such as roads are stored separately in the digital landscape model (DLM) and digital terrain model (DTM), which is often acquired by airborne laser scanning (ALS). However, an increasing number of applications require a combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, roads may be situated on strongly tilted DTM parts and rivers sometimes flow uphill. In this paper we propose an algorithm for the adaptation of 2D road centrelines to ALS data by means of network snakes. Generally, the image energy for the snakes is defined based on ALS intensity and height information and derived products. Additionally, buildings and bridges as strong features in height data are exploited in order to support the road adaptation process. Extracted buildings as priors modified by a distance transform are used to create a force of repulsion for the road vectors integrated in the image energy. In contrast, bridges give strong evidence for the correct road position in the height data. Therefore, the image energy is adapted for the bridge points. For that purpose bridge detection in the DTM is performed starting from an approximate position using template matching. Examples are given which apply the concept of network-snakes with new image energy for the adaptation of road networks to ALS data taking advantage of the prior known topology.