Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 897-902, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-897-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
22 Jun 2016
OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS
M. Maboudi1,2, J. Amini1, and M. Hahn2 1School of Surveying and Geospatial Engineering, College of Engineering - University of Tehran
2Department of Geomatics Engineering, University of Applied Sciences, Stuttgart, Germany
Keywords: Road extraction, Segmentation, Objects grouping, Object based image analysis, Maximal similarity region merging Abstract. Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods.

Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

Conference paper (PDF, 2006 KB)


Citation: Maboudi, M., Amini, J., and Hahn, M.: OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 897-902, https://doi.org/10.5194/isprs-archives-XLI-B7-897-2016, 2016.

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