Abstract
Abstract. This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.
Highlights
Automatic road and road centerline extraction from highresolution satellite imagery has been an interesting research topic in the field of remote sensing (RS) and geographic information systems (GIS)
Road extraction methods based on image processing techniques have a widespread variety from road tracking methods that are based on state of semi-automatic and automatic definition of seed points (Bonnefon et al 2002), morphological methods and filter techniques (Amini and Saradjian 2000; Amini et al 2002; Talbot and Appleton 2007), deformable contour models (Jeon et al 2000; Laptev et al 2000), segmentation and classification methods (Miao et al 2014a; Shi et al 2013), to artificial intelligent techniques such as neural network and genetic algorithms (Mokhtarzade and Zoej 2007)
Minimum Map Unit (MMU) is an argument which asset the ability of an algorithm to detect, identify and classify urban land-use patterns recorded on remotely sensed data
Summary
Automatic road and road centerline extraction from highresolution satellite imagery has been an interesting research topic in the field of remote sensing (RS) and geographic information systems (GIS). Miao et al (2014b) presented a semi-automatic method to extract road centerlines from VHR images. They argued that the surrounding objects, for instance occlusion of trees and shadows are problematic factors to optimize the extraction in high-resolution images. The integrated system is implemented automatically and the results show a completeness range between 70 % and 86 % with correctness range between 70 % and 92 % They believe that in highresolution images, the details such as street marking and cars provide additional context information, they disrupt extracting the overall road classes as they faced some errors in their results due to disturbances from the surrounding objects.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
