Automatic road network extraction in suburban areas from high resolution aerial images

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Grote, Anne; Rottensteiner, Franz: Automatic road network extraction in suburban areas from high resolution aerial images. 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, S. 299-304

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Sum total of downloads: 118

In this paper a road network extraction algorithm for suburban areas is presented. The algorithm uses colour infrared (CIR) images and digital surface models (DSM). The CIR data allow a good separation between vegetation and roads. The image is first segmented in two steps: an initial segmentation using the normalized cuts algorithm and a subsequent grouping of the segments. Road parts are extracted from the segments and then first connected locally to form subgraphs, because roads are often not extracted as a whole due to disturbances in their appearance. Subgraphs can contain several branches, which are resolved by a subsequent optimisation. The optimisation uses criteria describing the relations between the road parts as well as context objects such as trees, vehicles and buildings. The resulting road strings, represented by their centre lines, are then connected to a road network by searching for junctions at the ends of the roads. Small isolated roads are eliminated because they are likely to be false extractions. Results are presented for three image subsets coming from two different data sets, and a quantitative analysis of the completeness and correctness is shown from nine image subsets from the two data sets. The results show that the approach is suitable for the extraction of roads in suburban areas from aerial images.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2010
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 88 74.58%
2 image of flag of Korea, Republic of Korea, Republic of 6 5.08%
3 image of flag of China China 6 5.08%
4 image of flag of United States United States 4 3.39%
5 image of flag of Nepal Nepal 2 1.69%
6 image of flag of Estonia Estonia 2 1.69%
7 image of flag of No geo information available No geo information available 1 0.85%
8 image of flag of Singapore Singapore 1 0.85%
9 image of flag of Saudi Arabia Saudi Arabia 1 0.85%
10 image of flag of Canada Canada 1 0.85%
    other countries 6 5.08%

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