Please use this identifier to cite or link to this item:
|Title:||Delineation and geometric modeling of road networks||Authors:||You, Suya
|Keywords:||Aerial photographs;Human-computer interaction;Optical radar||Issue Date:||2010||Publisher:||Elsevier||Source:||ISPRS Journal of Photogrammetry and Remote Sensing, 2010, Volume 65, Issue 2, Pages 165-181||Abstract:||In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.||URI:||http://ktisis.cut.ac.cy/handle/10488/7140||ISSN:||09242716||DOI:||http://dx.doi.org/10.1016/j.isprsjprs.2009.10.004||Rights:||© International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Feb 13, 2018
checked on Aug 18, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.