Road Centerline Extraction From VHR Images Using SVM and Multi-Scale Maximum Response Filter

Abstract

In this work, an integrated framework comprising of pixel-based classification, road network filtering, and multi-scale Gabor filter is proposed to address the various prevailing issues in road centerline extraction from VHR images. The proposed framework is composed of three steps; generation of the initial road map, road network filtering and road centerline extraction. In the first step, pixel-based classification using support vector machine (SVM) is performed on VHR imagery to classify it into the road and non-road classes. In the road network filtering step, to retain the road features in classified imagery, an edge-preserving guided filter is applied and to improve the veracity of road extraction, undesirable components are eliminated by shape feature analysis. Finally, the complete and accurate road centerline are obtained by combining multi-scale Gabor filter and fast parallel thinning algorithm. The maximum response from a multi-scale Gabor filter is not only used for extraction but also used for reconstructing the broken road network, which is due to occlusions and artifacts. The proposed framework is implemented on publically available VHR images dataset. The simulation outcomes reflect the dominance of the proposed framework on different quantitative evaluation parameters as compared to up-to-date methods.