Network snakes for adapting GIS roads to height data of different data sources - performance analysis using als data and stereo images

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Göpfert, Jens; Rottensteiner, Franz; Heipke, Christian: Network snakes for adapting GIS roads to height data of different data sources - performance analysis using als data and stereo images. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3 (2012), Nr. 1, S. 209-214. DOI:

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Sum total of downloads: 42

In order to tackle the problem of consistently integrating 2D vector data and a DTM, we presented an approach for the adaptation of 2D GIS road objects to airborne laser scanning (ALS) data using active contours (snakes) in (Gopfert et al., 2011). In this paper the algorithm is modified for the integration of stereo images as an alternative data source for area-wide height information. For that reason, a new image energy is developed that exploits geometric and radiometric features derived from the image data. Afterwards, we compare the applicability of our method with respect to the ALS data and stereo images as input. In addition, a new approach is suggested that analyses the different energy terms of active contours after the optimisation process in order to automatically detect contour parts that did not reach a suitable position in the sensor data. This concept of an internal evaluation is able to guide the user during post processing. Experiments show that the snake approach with an image energy based on stereo images is generally able to adapt GIS road centrelines to the sensor data and thus to improve the quality of the 2D vector data. However, the comparison to the results for ALS data demonstrates that the algorithm perform slightly worse for image data in the high precision level.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2012
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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1 image of flag of Germany Germany 35 83.33%
2 image of flag of Nepal Nepal 2 4.76%
3 image of flag of Korea, Republic of Korea, Republic of 2 4.76%
4 image of flag of Austria Austria 2 4.76%
5 image of flag of Thailand Thailand 1 2.38%

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