
Proceedings Paper
Study on the extraction of urban roads from high-resolution remotely sensed imagery based on the knowledge of road featuresFormat | Member Price | Non-Member Price |
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Paper Abstract
This paper presents a novel road-extraction method focusing on a road network in an urban central area. The method
introduces the knowledge of road features into the extraction process and makes full use of spectral and spatial context
relationships and geometric information, thus successfully discriminates roads and spectrally similar buildings and solves
the problem of urban roads inconsistent morphology in the imagery. We adopt a Decision Tree model to extract the raw
roads information based on the spectral knowledge of pure pixel signatures. Then an "Eliminate & Growing" algorithm
is developed based on the context spatial relationships to make the roads independent and filled and reduce the "salt and
pepper" effects. Next, we retrieve more accurate road information in vector format in terms of the road's geometric
characteristics. Moreover, we manage to retrieve the hidden roads blocked by the trees via utilizing the information of
wayside trees. And finally we use mathematical morphology to form the road network. This method has successfully
extracted all the main and sub-main roads in the study area; the result has demonstrated the method's high accuracy and
usefulness in practice.
Paper Details
Date Published: 26 July 2007
PDF: 13 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521A (26 July 2007); doi: 10.1117/12.760685
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
PDF: 13 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521A (26 July 2007); doi: 10.1117/12.760685
Show Author Affiliations
Zhan Li, Nanjing Univ. (China)
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
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