GAP FILLING IN ROAD EXTRACTION USING RADON TRANSFORMATION
Road information has a key role in many applications such as transportation, automatic navigation, traffic management, crisis management, and also to facilitate and accelerate updating databases in a GIS. Therefore in the past two decades, automatic road extraction has become an important issue in r...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/47/2012/isprsannals-I-4-47-2012.pdf |
| Summary: | Road information has a key role in many applications such as transportation, automatic navigation, traffic management, crisis
management, and also to facilitate and accelerate updating databases in a GIS. Therefore in the past two decades, automatic road
extraction has become an important issue in remote sensing, photogrammetry and computer vision. An essential challenge in road
extraction process is filling the gaps which have appeared due to getting placed under trees, tunnels or any other reason. Connection
of roads is a momentous topological property that is necessity to perform most of the spatial analyses. Hence, Gap filling is an
important post-process. The main aim of this paper is to provide a method which is applicable in road extraction algorithms to
automatic fill the gaps. The proposed algorithm is based on Radon transformation and has four stags. In the first stage, detected road
are thinned insofar as one pixel width is achieved. Then endpoints are detected. In the second stage, regarding to some constraints
those endpoints which do not belong to any gaps are identified and deleted from endpoints list. In the third stage, the real gaps are
found using the road direction computed by used of Radon technique. In fourth stage, the selected endpoints are connected together
using Spline interpolation. This algorithm is applied on several datasets and also on a real detected road. The experimental results
show that the proposed algorithm has good performance on straight roads but it does not work well in intersections, due to being
direction-oriented. |
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| ISSN: | 2194-9042 2194-9050 |