Abstract
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.
Highlights
Gap filling in linear feature is an issue in digital image processing and computer vision
Since road continuity is a momentous topological property that is necessity to perform most of the spatial analyses, a post-processing stage should be applied in road extraction process to solve these problems
Several algorithms have been innovatively designed by researchers. (Hashemi et al, 2011) used fuzzy inference system to fill the gaps in detected road net. (Garcia, et al, 2008) used a Linear Method and a Polynomial Method in order to model straight roads and curves respectively. (Gardner, et al, 2001) applied a second type of Q-tree filter to reclassify some vegetation pixels that occur along classified road pixels as road. (Mnih and Hinton, 2010) train a convolutional neural network to removes disconnected blotches and fills in the gaps in the roads
Summary
Gap filling in linear feature is an issue in digital image processing and computer vision. Gap filling is used in various fields such as medical image processing (Szymczak, 2005; Risser, 2008; Akhras, 2007), linear feature extraction from raster maps (Khotanzad and zink, 2003; Pouderoux and Spinello, 2007; Chiang, et al, 2005; Chiang, et al, 2008; Linton, 2009; Henderson and Linton, 2009) and remotely sensed data. Many studies have been done on road extraction from remotely sensed data (Mena, 2003), but none of them can extract road network perfectly This is due to many reasons such as algorithm weakness, sensor geometry, occlusions, shadows, and a wide variety of non-road objects (Garcia, et al, 2008; Mnih and Hinton, 2010; Hashemi et al, 2011). Several algorithms have been innovatively designed by researchers. (Hashemi et al, 2011) used fuzzy inference system to fill the gaps in detected road net. (Garcia, et al, 2008) used a Linear Method and a Polynomial Method in order to model straight roads and curves respectively. (Gardner, et al, 2001) applied a second type of Q-tree filter to reclassify some vegetation pixels that occur along classified road pixels as road. (Mnih and Hinton, 2010) train a convolutional neural network to removes disconnected blotches and fills in the gaps in the roads
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