Road Extraction Using SVM and Image Segmentation

Abstract

Accurate road information is vital for transportation applications, including as part of geographical information systems (GIS). This article reports on the development of a two-step approach for road extraction that utilizes pixel spectral information for classification and image segmentation-derived object features. In the first step, support vector machine (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. For this classification, support vector machine (SVM) achieved higher accuracy than Gaussian maximum likelihood (GML). In the second step, the road group image was segmented into geometrically homogeneous objects using a region growing technique based on a similarity criterion, with higher weighting on shape factors over spectral criteria. A simple thresholding on the shape index and density features derived from these objects was performed to extract road features, which were further processed by thinning and vectorization to obtain road centerlines. The authors conclude that the proposed approach worked well with images comprised by both rural and urban area features.