Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images

Abstract

Road network delineation from remote sensing imagery is useful in many applications related to humans in present days as it provides meaningful information which is very helpful in the field of transportation and other support systems of human civilization. However, the road network extraction from remote sensing images is a challenging problem because of various properties of road network altered by different factors. In this paper, a supervised multistage framework based on least square support vector machine (LS-SVM), mathematical morphology and road shape features is proposed to extract road networks from remote sensing images. In the first stage, after denoising the image using the nonlinear filter, the image is segmented using LS-SVM into two classes’ road or non-road but some non-road components like building structures, shadows of trees and other roads like objects are also classified as the road network. In the second stage, the morphology and primary shape features are used to remove the non-road objects. Finally, road centerlines are extracted using a method based on Euclidean distance transformation. The experimental results reflect the accuracy of the proposed work as compared to other road extraction methods.