Preprogrammed Road Extraction from Satellite Images using Adaptive Texture Matching-Region (ATM-R) growing Approach

Abstract

In this paper we present a novel programmed road extraction algorithm based on adaptive texture
matching (ATM-R), which is a variant of region growing approach. The calculation created in this paper is
hearty to varieties in radiometric goal of info pictures, street surface qualities (surface) and street geometry
and is adaptable to contributions from various sensors. In the proposed algorithm, difference in a pair of
closely dated multi-temporal images of same geographic area has been studied to generate road templates
(seeds) and such templates are further utilized to extract remaining road region within a road template
matching framework which adapts to local road texture. To assess the performance of the proposed
algorithm, a set of images encompassing wide range of radiometric resolutions and different spatial
resolutions were prepared. Analyses were led on pictures with separated street sections, multi-path streets,
street districts blocked with trees and pictures with street over-spans. The measures used to evaluate the
algorithm performance are road network completeness, road area completeness and correctness of road area.
The algorithm is able to consistently extract 70% to 90% of the road network and has a high performance
against all the three measures. Along with generating road network the proposed algorithm is able to extract
road area which can be further used to assess road width, number of road lanes and other auxiliary road
network information. The algorithm has considerably reduced manual intervention in road extraction
process and avoided the need for GCPs (ground control points).