GAP FILLING IN ROAD EXTRACTION USING RADON TRANSFORMATION
- 1Remote Sensing & GIS Department, Shahid Beheshti University, Evin, Tehran, Iran
- 2Cognitive Telecommunications Group, Department of Electrical and Computer Engineering, Shahid Beheshti University, Evin, Tehran, Iran
- 3Geoinformation Science School of Mathematical and Geospatial Sciences. RMIT University, Melbourne, Australia
- 4Geomatics College of National Cartographic Center, Azadi sq. Tehran, Iran
Keywords: Urban, automation, algorithm, Extraction, Transformation
Abstract. 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.