By Topic

Modeling contextual knowledge for controlling road extraction in urban areas

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hinz, S. ; Photogmmmetry & Remote Sensing, Technische Univ. Munchen, Germany ; Baumgartner, A. ; Ebner, H.

This paper deals with the role of context for automatic extraction of man-made structures from aerial images taken over urban areas. Due to the intrinsic high complexity of urban scenes we propose to guide the extraction by contextual knowledge about the objects. We represent this knowledge explicitly by a context model. Based upon this model we are able to split the complex task of object extraction in urban areas into smaller sub-problems. The novelty presented in this contribution mainly relates to the fact that essential contextual information is gathered at the beginning of the extraction, thus, it is available during the whole extraction, and furthermore, it allows for automatically controlling the extraction process: for data consistency reasons, we use the imagery as the only source for both gaining contextual information and extracting roads. Advantages and remaining deficiencies of the proposed strategy are discussed

Published in:

Remote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop 2001

Date of Conference: